2) Final data-set development
This section details how our final data-set was developed. Our final
data set included cognitively healthy participants at baseline, FU1, and
FU2 with complete baseline neuropsycholoigcal testing (including
education level, which was necessary for standardized scores). Thus our
final sample size included in our linear mixed models was N=11,355.
Track.Final_Data <- subset(Track.Full, SCI_0=="No" & Dementia_0=="No" & SCI_1=="No" & Dementia_1 =="No" &
SCI_2=="No" & Dementia_2 == "No" & !is.na(Animal_Fluency_Strict_0) & !is.na(MAT_Score_0) & !is.na(Education4_0) &
!is.na(RVLT_Immediate_Score_0) & !is.na(RVLT_Delayed_Score_0))
We then normalized all cognitive variables for language, age, and
biological sex. Score standardization is dependent upon the following
steps:
- Create a predicted score based on age, sex, and test language
- Create residualized score (i.e., obtained - predicted)
- Develop a standardized residual score adjusted for age, sex, and
education
- Develop standardized z-score adjusted for age, sex, and
education
- Develop standardized score using z-score with a mean of 10 and a SD
of 3.
Tracking.Adjusted_Full <- Track.Final_Data %>%
mutate(
#Baseline Scores
#RVLT Immediate Score
RVLT_Immediate_Predicted_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 7.768 - 0.050*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 7.449 - 0.036*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ 10.095 - 0.073*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ 9.686 - 0.064*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 8.077 - 0.043*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 9.806 - 0.059*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ 9.161 - 0.047*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ 9.804 - 0.053*Age_0,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 5.666 - 0.025*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 8.953 - 0.067*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ 7.662 - 0.039*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ 8.829 - 0.057*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 6.976 - 0.031*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 8.667 - 0.045*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ 9.502 - 0.061*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ 9.013 - 0.047*Age_0
),
RVLT_Immediate_Residual_0 = RVLT_Immediate_Score_0 - RVLT_Immediate_Predicted_0,
RVLT_Immediate_Z_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.471,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.525,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.611,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.675,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.528,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.643,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.694,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.802,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.290,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.473,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.913,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.641,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.623,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.595,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.605,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.715
),
RVLT_Immediate_Normed_0 = RVLT_Immediate_Z_0*3 +10,
RVLT_Immediate_Normed_0 = if_else(RVLT_Immediate_Normed_0 < 0, 0.01, RVLT_Immediate_Normed_0),
#RVLT Delayed Score
RVLT_Delayed_Predicted_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 6.628 - 0.062*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 6.851 - 0.058*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ 8.289 - 0.076*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ 8.165 - 0.070*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 7.163 - 0.055*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 8.115 - 0.063*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ 8.151 - 0.059*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ 8.844 - 0.066*Age_0,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 4.802 - 0.036*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 8.219 - 0.083*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ 9.721 - 0.097*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ 7.048 - 0.055*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 6.280 - 0.044*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 6.999 - 0.042*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ 9.081 - 0.074*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ 8.712 - 0.066*Age_0
),
RVLT_Delayed_Residual_0 = RVLT_Delayed_Score_0 - RVLT_Delayed_Predicted_0,
RVLT_Delayed_Z_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.534,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.739,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.802,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.890,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.787,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/2.005,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.869,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/2.135,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.559,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.571,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.815,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.721,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.859,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.793,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.901,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.890
),
RVLT_Delayed_Normed_0 = RVLT_Delayed_Z_0*3 +10,
RVLT_Delayed_Normed_0 = if_else(RVLT_Delayed_Normed_0 < 0, 0.01, RVLT_Delayed_Normed_0),
#Animal Fluency
Animal_Fluency_Predicted_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 23.132 - 0.095*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 28.923 - 0.157*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ 32.513 - 0.202*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ 31.143 - 0.168*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 23.433 - 0.114*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 29.912 - 0.181*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ 30.764 - 0.178*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ 32.003 - 0.186*Age_0,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 26.034 - 0.152*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 33.358 - 0.241*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ 36.511 - 0.277*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ 30.193 - 0.179*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 21.460 - 0.089*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 21.355 - 0.070*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ 30.881 - 0.205*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ 29.961 - 0.180*Age_0
),
Animal_Fluency_Residual_0 = Animal_Fluency_Lenient_0 - Animal_Fluency_Predicted_0,
Animal_Fluency_Z_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.145,
Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.348,
Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.163,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.354,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/4.665,
Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/4.728,
Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.176,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.369,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/3.911,
Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.889,
Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/5.061,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.869,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.178,
Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.321,
Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.468,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.940
),
Animal_Fluency_Normed_0 = Animal_Fluency_Z_0*3 +10,
Animal_Fluency_Normed_0 = if_else(Animal_Fluency_Normed_0 < 0, 0.01, Animal_Fluency_Normed_0),
#Mental Alteration Test
MAT_Predicted_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ 33.295 - 0.161*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ 34.074 - 0.123*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ 41.488 - 0.219*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ 40.573 - 0.190*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ 39.102 - 0.251*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ 41.657 - 0.246*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ 36.877 - 0.168*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ 38.849 - 0.188*Age_0,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ 36.630 - 0.252*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ 38.784 - 0.181*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ 51.105 - 0.381*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ 44.106 - 0.257*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ 34.814 - 0.214*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ 38.756 - 0.202*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ 47.024 - 0.315*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ 41.717 - 0.234*Age_0
),
MAT_Residual_0 = MAT_Score_0 - MAT_Predicted_0,
MAT_Z_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.602,
Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.702,
Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.490,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.727,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.080,
Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.139,
Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ MAT_Residual_0/6.915,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ MAT_Residual_0/6.979,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.589,
Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.234,
Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.314,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.609,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.803,
Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.079,
Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.451,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.734
),
MAT_Normed_0 = MAT_Z_0*3 +10,
MAT_Normed_0 = if_else(MAT_Normed_0 < 0, 0.01, MAT_Normed_0),
#Global Cognition Composite Score
Global_Composite_0 = RVLT_Immediate_Z_0 + RVLT_Delayed_Z_0 + Animal_Fluency_Z_0 + MAT_Z_0,
#FU1 Scores
#RVLT Immediate Score
RVLT_Immediate_Predicted_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 7.768 - 0.050*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 7.449 - 0.036*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ 10.095 - 0.073*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ 9.686 - 0.064*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 8.077 - 0.043*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 9.806 - 0.059*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ 9.161 - 0.047*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ 9.804 - 0.053*Age_1,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 5.666 - 0.025*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 8.953 - 0.067*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ 7.662 - 0.039*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ 8.829 - 0.057*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 6.976 - 0.031*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 8.667 - 0.045*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ 9.502 - 0.061*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ 9.013 - 0.047*Age_1
),
RVLT_Immediate_Residual_1 = RVLT_Immediate_Score_1 - RVLT_Immediate_Predicted_1,
RVLT_Immediate_Z_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.471,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.525,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.611,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.675,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.528,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.643,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.694,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.802,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.290,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.473,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.913,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.641,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.623,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.595,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.605,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.715
),
RVLT_Immediate_Normed_1 = RVLT_Immediate_Z_1*3 + 10,
RVLT_Immediate_Normed_1 = if_else(RVLT_Immediate_Normed_1 < 0, 0.01, RVLT_Immediate_Normed_1),
#RVLT Delayed Score
RVLT_Delayed_Predicted_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 6.628 - 0.062*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 6.851 - 0.058*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ 8.289 - 0.076*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ 8.165 - 0.070*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 7.163 - 0.055*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 8.115 - 0.063*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ 8.151 - 0.059*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ 8.844 - 0.066*Age_1,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 4.802 - 0.036*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 8.219 - 0.083*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ 9.721 - 0.097*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ 7.048 - 0.055*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 6.280 - 0.044*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 6.999 - 0.042*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ 9.081 - 0.074*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ 8.712 - 0.066*Age_1
),
RVLT_Delayed_Residual_1 = RVLT_Delayed_Score_1 - RVLT_Delayed_Predicted_1,
RVLT_Delayed_Z_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.534,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.739,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.802,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.890,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.787,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/2.005,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.869,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/2.135,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.559,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.571,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.815,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.721,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.859,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.793,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.901,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.890
),
RVLT_Delayed_Normed_1 = RVLT_Delayed_Z_1*3 + 10,
RVLT_Delayed_Normed_1 = if_else(RVLT_Delayed_Normed_1 < 0, 0.01, RVLT_Delayed_Normed_1),
#Animal Fluency
Animal_Fluency_Predicted_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 23.132 - 0.095*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 28.923 - 0.157*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ 32.513 - 0.202*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ 31.143 - 0.168*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 23.433 - 0.114*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 29.912 - 0.181*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ 30.764 - 0.178*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ 32.003 - 0.186*Age_1,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 26.034 - 0.152*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 33.358 - 0.241*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ 36.511 - 0.277*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ 30.193 - 0.179*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 21.460 - 0.089*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 21.355 - 0.070*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ 30.881 - 0.205*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ 29.961 - 0.180*Age_1
),
Animal_Fluency_Residual_1 = Animal_Fluency_Lenient_1 - Animal_Fluency_Predicted_1,
Animal_Fluency_Z_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.145,
Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.348,
Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.163,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.354,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/4.665,
Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/4.728,
Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.176,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.369,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/3.911,
Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.889,
Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/5.061,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.869,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.178,
Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.321,
Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.468,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.940
),
Animal_Fluency_Normed_1 = Animal_Fluency_Z_1*3 + 10,
Animal_Fluency_Normed_1 = if_else(Animal_Fluency_Normed_1 < 0, 0.01, Animal_Fluency_Normed_1),
#Mental Alteration Test
MAT_Predicted_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ 33.295 - 0.161*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ 34.074 - 0.123*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ 41.488 - 0.219*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ 40.573 - 0.190*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ 39.102 - 0.251*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ 41.657 - 0.246*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ 36.877 - 0.168*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ 38.849 - 0.188*Age_1,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ 36.630 - 0.252*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ 38.784 - 0.181*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ 51.105 - 0.381*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ 44.106 - 0.257*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ 34.814 - 0.214*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ 38.756 - 0.202*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ 47.024 - 0.315*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ 41.717 - 0.234*Age_1
),
MAT_Residual_1 = MAT_Score_1 - MAT_Predicted_1,
MAT_Z_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.602,
Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.702,
Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.490,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.727,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.080,
Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.139,
Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ MAT_Residual_1/6.915,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ MAT_Residual_1/6.979,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ MAT_Residual_1/7.589,
Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ MAT_Residual_1/7.234,
Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ MAT_Residual_1/6.314,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ MAT_Residual_1/7.609,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ MAT_Residual_1/6.803,
Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ MAT_Predicted_1/7.079,
Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ MAT_Predicted_1/6.451,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ MAT_Predicted_1/6.734
),
MAT_Normed_1 = MAT_Z_1*3 + 10,
MAT_Normed_1 = if_else(MAT_Normed_1 < 0, 0.01, MAT_Normed_1),
#Global Cognition Composite Score
Global_Composite_1 = RVLT_Immediate_Z_1 + RVLT_Delayed_Z_1 + Animal_Fluency_Z_1 + MAT_Z_1,
#FU2 Scores
#RVLT Immediate Score
RVLT_Immediate_Predicted_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 7.768 - 0.050*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 7.449 - 0.036*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ 10.095 - 0.073*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ 9.686 - 0.064*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 8.077 - 0.043*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 9.806 - 0.059*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ 9.161 - 0.047*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ 9.804 - 0.053*Age_2,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 5.666 - 0.025*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 8.953 - 0.067*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ 7.662 - 0.039*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ 8.829 - 0.057*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 6.976 - 0.031*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 8.667 - 0.045*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ 9.502 - 0.061*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ 9.013 - 0.047*Age_2
),
RVLT_Immediate_Residual_2 = RVLT_Immediate_Score_2 - RVLT_Immediate_Predicted_2,
RVLT_Immediate_Z_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.471,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.525,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.611,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.675,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.528,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.643,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.694,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.802,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.290,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.473,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.913,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.641,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.623,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.595,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.605,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.715
),
RVLT_Immediate_Normed_2 = RVLT_Immediate_Z_2*3 + 10,
RVLT_Immediate_Normed_2 = if_else(RVLT_Immediate_Normed_2 < 0, 0.01, RVLT_Immediate_Normed_2),
#RVLT Delayed Score
RVLT_Delayed_Predicted_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 6.628 - 0.062*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 6.851 - 0.058*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ 8.289 - 0.076*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ 8.165 - 0.070*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 7.163 - 0.055*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 8.115 - 0.063*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ 8.151 - 0.059*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ 8.844 - 0.066*Age_2,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 4.802 - 0.036*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 8.219 - 0.083*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ 9.721 - 0.097*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ 7.048 - 0.055*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 6.280 - 0.044*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 6.999 - 0.042*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ 9.081 - 0.074*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ 8.712 - 0.066*Age_2
),
RVLT_Delayed_Residual_2 = RVLT_Delayed_Score_2 - RVLT_Delayed_Predicted_2,
RVLT_Delayed_Z_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.534,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.739,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.802,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.890,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.787,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/2.005,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.869,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/2.135,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.559,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.571,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.815,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.721,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.859,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.793,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.901,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.890
),
RVLT_Delayed_Normed_2 = RVLT_Delayed_Z_2*3 + 10,
RVLT_Delayed_Normed_2 = if_else(RVLT_Delayed_Normed_2 < 0, 0.01, RVLT_Delayed_Normed_2),
#Animal Fluency
Animal_Fluency_Predicted_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 23.132 - 0.095*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 28.923 - 0.157*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ 32.513 - 0.202*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ 31.143 - 0.168*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 23.433 - 0.114*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 29.912 - 0.181*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ 30.764 - 0.178*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ 32.003 - 0.186*Age_2,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 26.034 - 0.152*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 33.358 - 0.241*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ 36.511 - 0.277*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ 30.193 - 0.179*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 21.460 - 0.089*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 21.355 - 0.070*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ 30.881 - 0.205*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ 29.961 - 0.180*Age_2
),
Animal_Fluency_Residual_2 = Animal_Fluency_Lenient_2 - Animal_Fluency_Predicted_2,
Animal_Fluency_Z_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.145,
Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.348,
Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.163,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.354,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/4.665,
Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/4.728,
Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.176,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.369,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/3.911,
Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.889,
Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/5.061,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.869,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.178,
Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.321,
Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.468,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.940
),
Animal_Fluency_Normed_2 = Animal_Fluency_Z_2*3 + 10,
Animal_Fluency_Normed_2 = if_else(Animal_Fluency_Normed_2 < 0, 0.01, Animal_Fluency_Normed_2),
#Mental Alteration Test
MAT_Predicted_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ 33.295 - 0.161*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ 34.074 - 0.123*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ 41.488 - 0.219*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ 40.573 - 0.190*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ 39.102 - 0.251*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ 41.657 - 0.246*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ 36.877 - 0.168*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ 38.849 - 0.188*Age_2,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ 36.630 - 0.252*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ 38.784 - 0.181*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ 51.105 - 0.381*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ 44.106 - 0.257*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ 34.814 - 0.214*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ 38.756 - 0.202*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ 47.024 - 0.315*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ 41.717 - 0.234*Age_2
),
MAT_Residual_2 = MAT_Score_2 - MAT_Predicted_2,
MAT_Z_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.602,
Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.702,
Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.490,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.727,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.080,
Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.139,
Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ MAT_Residual_2/6.915,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ MAT_Residual_2/6.979,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.589,
Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.234,
Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.314,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.609,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.803,
Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.079,
Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.451,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.734
),
MAT_Normed_2 = MAT_Z_2*3 + 10,
MAT_Normed_2 = if_else(MAT_Normed_2 < 0, 0.01, MAT_Normed_2),
#Global Cognition Composite Score
Global_Composite_2 = RVLT_Immediate_Z_2 + RVLT_Delayed_Z_2 + Animal_Fluency_Z_0 + MAT_Z_2
)
Finally, we categorized participants as having their follow-up 2 data
collected before or after the start of the COVID-19 pandemic.
Tracking.Adjusted_Final <- Tracking.Adjusted_Full %>%
mutate(timestamp = ymd_hms(startdate_TRF2_2, tz = "EST"))
## Date in ISO8601 format; converting timezone from UTC to "EST".
start_time <- as.POSIXct("2020-03-11 00:00:00", tz = "EST")
Tracking.Adjusted_Final$Pandemic<-NA
Tracking.Adjusted_Final$Pandemic[Tracking.Adjusted_Final$timestamp>=start_time]<-"FU2 data collected after COVID-19"
Tracking.Adjusted_Final$Pandemic[Tracking.Adjusted_Final$timestamp<start_time]<-"FU2 data collected before COVID-19"
2.1) Flow-chart for participants
Full tracking cohort at baseline (N=21,241)
Track.Baseline.total <- Track.Baseline.Final %>%
count()
print(Track.Baseline.total)
## n
## 1 21241
Excluding tracking cohort participants lost to follow-up
(N=14,697)
Track.Complete <- Track.Full %>%
count()
print(Track.Complete)
## n
## 1 14697
Baseline tracking cohort removing individuals who reported SCI or
dementia at baseline, FU1, or FU2 (N=13,934)
Track.SCI_Dementia <- Track.Full %>%
group_by(SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2) %>%
count()
print(Track.SCI_Dementia)
## # A tibble: 40 × 7
## # Groups: SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2 [40]
## SCI_0 Dementia_0 SCI_1 Dementia_1 SCI_2 Dementia_2 n
## <chr> <chr> <chr> <chr> <chr> <chr> <int>
## 1 No No No No No No 13934
## 2 No No No No No Yes 7
## 3 No No No No No <NA> 7
## 4 No No No No Yes No 143
## 5 No No No No Yes Yes 18
## 6 No No No No Yes <NA> 2
## 7 No No No No <NA> No 15
## 8 No No No No <NA> Yes 1
## 9 No No No No <NA> <NA> 160
## 10 No No No Yes No <NA> 3
## # ℹ 30 more rows
Baseline tracking cohort removing individuals with dementia or SCI
and without full cog data (N=11,355)
Track.FullCogs <- Track.Full %>%
group_by(SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2,
!is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0), !is.na(RVLT_Delayed_Score_0),!is.na(Animal_Fluency_Strict_0)) %>%
count()
print(Track.FullCogs)
## # A tibble: 105 × 11
## # Groups: SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2,
## # !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0),
## # !is.na(RVLT_Delayed_Score_0), !is.na(Animal_Fluency_Strict_0) [105]
## SCI_0 Dementia_0 SCI_1 Dementia_1 SCI_2 Dementia_2 `!is.na(MAT_Score_0)`
## <chr> <chr> <chr> <chr> <chr> <chr> <lgl>
## 1 No No No No No No FALSE
## 2 No No No No No No FALSE
## 3 No No No No No No FALSE
## 4 No No No No No No FALSE
## 5 No No No No No No FALSE
## 6 No No No No No No FALSE
## 7 No No No No No No FALSE
## 8 No No No No No No FALSE
## 9 No No No No No No TRUE
## 10 No No No No No No TRUE
## # ℹ 95 more rows
## # ℹ 4 more variables: `!is.na(RVLT_Immediate_Score_0)` <lgl>,
## # `!is.na(RVLT_Delayed_Score_0)` <lgl>,
## # `!is.na(Animal_Fluency_Strict_0)` <lgl>, n <int>
Final sample size (N=11,355) with complete cognitive data at FU1
grouped by Pandemic cohort
Track.FullCogs2 <- Tracking.Adjusted_Final %>%
group_by(!is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0), !is.na(RVLT_Delayed_Score_0),!is.na(Animal_Fluency_Strict_0),
!is.na(MAT_Score_1), !is.na(RVLT_Immediate_Score_1), !is.na(RVLT_Delayed_Score_1),!is.na(Animal_Fluency_Strict_1), Pandemic) %>%
count()
print(Track.FullCogs2)
## # A tibble: 29 × 10
## # Groups: !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0),
## # !is.na(RVLT_Delayed_Score_0), !is.na(Animal_Fluency_Strict_0),
## # !is.na(MAT_Score_1), !is.na(RVLT_Immediate_Score_1),
## # !is.na(RVLT_Delayed_Score_1), !is.na(Animal_Fluency_Strict_1), Pandemic
## # [29]
## `!is.na(MAT_Score_0)` `!is.na(RVLT_Immediate_Score_0)` !is.na(RVLT_Delayed_…¹
## <lgl> <lgl> <lgl>
## 1 TRUE TRUE TRUE
## 2 TRUE TRUE TRUE
## 3 TRUE TRUE TRUE
## 4 TRUE TRUE TRUE
## 5 TRUE TRUE TRUE
## 6 TRUE TRUE TRUE
## 7 TRUE TRUE TRUE
## 8 TRUE TRUE TRUE
## 9 TRUE TRUE TRUE
## 10 TRUE TRUE TRUE
## # ℹ 19 more rows
## # ℹ abbreviated name: ¹`!is.na(RVLT_Delayed_Score_0)`
## # ℹ 7 more variables: `!is.na(Animal_Fluency_Strict_0)` <lgl>,
## # `!is.na(MAT_Score_1)` <lgl>, `!is.na(RVLT_Immediate_Score_1)` <lgl>,
## # `!is.na(RVLT_Delayed_Score_1)` <lgl>,
## # `!is.na(Animal_Fluency_Strict_1)` <lgl>, Pandemic <chr>, n <int>
Final sample size (N=11,355) with complete cognitive data at FU2
grouped by Pandemic cohort
Track.FullCogs3 <- Tracking.Adjusted_Final %>%
group_by(!is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0), !is.na(RVLT_Delayed_Score_0),!is.na(Animal_Fluency_Strict_0),
!is.na(MAT_Score_2), !is.na(RVLT_Immediate_Score_2), !is.na(RVLT_Delayed_Score_2),!is.na(Animal_Fluency_Strict_2), Pandemic) %>%
count()
print(Track.FullCogs3)
## # A tibble: 25 × 10
## # Groups: !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0),
## # !is.na(RVLT_Delayed_Score_0), !is.na(Animal_Fluency_Strict_0),
## # !is.na(MAT_Score_2), !is.na(RVLT_Immediate_Score_2),
## # !is.na(RVLT_Delayed_Score_2), !is.na(Animal_Fluency_Strict_2), Pandemic
## # [25]
## `!is.na(MAT_Score_0)` `!is.na(RVLT_Immediate_Score_0)` !is.na(RVLT_Delayed_…¹
## <lgl> <lgl> <lgl>
## 1 TRUE TRUE TRUE
## 2 TRUE TRUE TRUE
## 3 TRUE TRUE TRUE
## 4 TRUE TRUE TRUE
## 5 TRUE TRUE TRUE
## 6 TRUE TRUE TRUE
## 7 TRUE TRUE TRUE
## 8 TRUE TRUE TRUE
## 9 TRUE TRUE TRUE
## 10 TRUE TRUE TRUE
## # ℹ 15 more rows
## # ℹ abbreviated name: ¹`!is.na(RVLT_Delayed_Score_0)`
## # ℹ 7 more variables: `!is.na(Animal_Fluency_Strict_0)` <lgl>,
## # `!is.na(MAT_Score_2)` <lgl>, `!is.na(RVLT_Immediate_Score_2)` <lgl>,
## # `!is.na(RVLT_Delayed_Score_2)` <lgl>,
## # `!is.na(Animal_Fluency_Strict_2)` <lgl>, Pandemic <chr>, n <int>
Final sample size (N=11,355) and number of participants with PASE
score at baseline (N=9,181)
PASEBL <- Tracking.Adjusted_Final %>%
group_by(!is.na(PASE_TOTAL_0), Pandemic) %>%
count()
print(PASEBL)
## # A tibble: 4 × 3
## # Groups: !is.na(PASE_TOTAL_0), Pandemic [4]
## `!is.na(PASE_TOTAL_0)` Pandemic n
## <lgl> <chr> <int>
## 1 FALSE FU2 data collected after COVID-19 1027
## 2 FALSE FU2 data collected before COVID-19 1147
## 3 TRUE FU2 data collected after COVID-19 4154
## 4 TRUE FU2 data collected before COVID-19 5027
Final sample size (N=11,355) and number of participants with PASE
score at baseline or FU1 (N=1,945)
PASEBL <- Tracking.Adjusted_Final %>%
group_by(!is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1),Pandemic) %>%
count()
print(PASEBL)
## # A tibble: 8 × 4
## # Groups: !is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1), Pandemic [8]
## `!is.na(PASE_TOTAL_0)` `!is.na(PASE_TOTAL_1)` Pandemic n
## <lgl> <lgl> <chr> <int>
## 1 FALSE FALSE FU2 data collected after … 882
## 2 FALSE FALSE FU2 data collected before… 1016
## 3 FALSE TRUE FU2 data collected after … 145
## 4 FALSE TRUE FU2 data collected before… 131
## 5 TRUE FALSE FU2 data collected after … 3226
## 6 TRUE FALSE FU2 data collected before… 4010
## 7 TRUE TRUE FU2 data collected after … 928
## 8 TRUE TRUE FU2 data collected before… 1017
Final sample size (N=11,355) and number of participants with PASE
score at baseline, FU1, or FU2 (N=762)
PASEBL <- Tracking.Adjusted_Final %>%
group_by(!is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1), , !is.na(PASE_TOTAL_2),Pandemic) %>%
count()
print(PASEBL)
## # A tibble: 16 × 5
## # Groups: !is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1), !is.na(PASE_TOTAL_2),
## # Pandemic [16]
## `!is.na(PASE_TOTAL_0)` `!is.na(PASE_TOTAL_1)` `!is.na(PASE_TOTAL_2)` Pandemic
## <lgl> <lgl> <lgl> <chr>
## 1 FALSE FALSE FALSE FU2 dat…
## 2 FALSE FALSE FALSE FU2 dat…
## 3 FALSE FALSE TRUE FU2 dat…
## 4 FALSE FALSE TRUE FU2 dat…
## 5 FALSE TRUE FALSE FU2 dat…
## 6 FALSE TRUE FALSE FU2 dat…
## 7 FALSE TRUE TRUE FU2 dat…
## 8 FALSE TRUE TRUE FU2 dat…
## 9 TRUE FALSE FALSE FU2 dat…
## 10 TRUE FALSE FALSE FU2 dat…
## 11 TRUE FALSE TRUE FU2 dat…
## 12 TRUE FALSE TRUE FU2 dat…
## 13 TRUE TRUE FALSE FU2 dat…
## 14 TRUE TRUE FALSE FU2 dat…
## 15 TRUE TRUE TRUE FU2 dat…
## 16 TRUE TRUE TRUE FU2 dat…
## # ℹ 1 more variable: n <int>
Final sample size (N=11,355) and number of participants with sleep
data at baseline (N=11,334)
PASEBL <- Tracking.Adjusted_Final %>%
group_by(!is.na(RSTLS_Sleep_0),Pandemic) %>%
count()
print(PASEBL)
## # A tibble: 4 × 3
## # Groups: !is.na(RSTLS_Sleep_0), Pandemic [4]
## `!is.na(RSTLS_Sleep_0)` Pandemic n
## <lgl> <chr> <int>
## 1 FALSE FU2 data collected after COVID-19 11
## 2 FALSE FU2 data collected before COVID-19 10
## 3 TRUE FU2 data collected after COVID-19 5170
## 4 TRUE FU2 data collected before COVID-19 6164
Final sample size (N=11,355) and number of participants with sleep
data at baseline or FU1 (N=11,304)
PASEBL <- Tracking.Adjusted_Final %>%
group_by(!is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1),Pandemic) %>%
count()
print(PASEBL)
## # A tibble: 6 × 4
## # Groups: !is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1), Pandemic [6]
## `!is.na(RSTLS_Sleep_0)` `!is.na(RSTLS_Sleep_1)` Pandemic n
## <lgl> <lgl> <chr> <int>
## 1 FALSE TRUE FU2 data collected afte… 11
## 2 FALSE TRUE FU2 data collected befo… 10
## 3 TRUE FALSE FU2 data collected afte… 7
## 4 TRUE FALSE FU2 data collected befo… 23
## 5 TRUE TRUE FU2 data collected afte… 5163
## 6 TRUE TRUE FU2 data collected befo… 6141
Final sample size (N=11,355) and number of participants with sleep
data at baseline, FU1, or FU2 (N=11,270)
PASEBL <- Tracking.Adjusted_Final %>%
group_by(!is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1), , !is.na(RSTLS_Sleep_2),Pandemic) %>%
count()
print(PASEBL)
## # A tibble: 8 × 5
## # Groups: !is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1),
## # !is.na(RSTLS_Sleep_2), Pandemic [8]
## `!is.na(RSTLS_Sleep_0)` !is.na(RSTLS_Sleep_1…¹ !is.na(RSTLS_Sleep_2…² Pandemic
## <lgl> <lgl> <lgl> <chr>
## 1 FALSE TRUE TRUE FU2 dat…
## 2 FALSE TRUE TRUE FU2 dat…
## 3 TRUE FALSE TRUE FU2 dat…
## 4 TRUE FALSE TRUE FU2 dat…
## 5 TRUE TRUE FALSE FU2 dat…
## 6 TRUE TRUE FALSE FU2 dat…
## 7 TRUE TRUE TRUE FU2 dat…
## 8 TRUE TRUE TRUE FU2 dat…
## # ℹ abbreviated names: ¹`!is.na(RSTLS_Sleep_1)`, ²`!is.na(RSTLS_Sleep_2)`
## # ℹ 1 more variable: n <int>
#2.2) Participants with positive COVID test
Sample of participants w/ COVID data
Track.Full.COVID.Final <- subset(Track.Full.COVID, SCI_0=="No" & Dementia_0=="No" & SCI_1=="No" & Dementia_1 =="No" &
SCI_2=="No" & Dementia_2 == "No" & !is.na(Animal_Fluency_Strict_0) & !is.na(MAT_Score_0) & !is.na(Education4_0) &
!is.na(RVLT_Immediate_Score_0) & !is.na(RVLT_Delayed_Score_0))
Participants w/ or w/out positive COVID-19 test (Yes = 1; No = 2;
Results not available = 3; 8 = Don’t know; -9999 = No data)
Covid.test <- Track.Full.COVID.Final %>%
group_by((SYM_TESTPOS_COVID)) %>%
count()
print(Covid.test)
## # A tibble: 5 × 2
## # Groups: (SYM_TESTPOS_COVID) [5]
## `(SYM_TESTPOS_COVID)` n
## <int> <int>
## 1 -99999 7397
## 2 1 7
## 3 2 95
## 4 3 7
## 5 8 1
3) Participant characteristics at baseline
Create factor variables for PASE sedentary behaviour and sleep
score
BL.data<-Tracking.Adjusted_Final
BL.data$PASE_Q1B_0 <- as.factor(ifelse(BL.data$PASE_Q1B_0==10, 1, 0))
BL.data$RSTLS_Sleep_0 <- as.factor(BL.data$RSTLS_Sleep_0)
Final baseline sample (N= 11,355)
Baseline<-dput(names(BL.data[c(5,4,14,12,6,7,8,9,10,11,15,18,19,21,98,102,106,110,29,31,27,24,13,22,23)]))
## c("Age_0", "Sex_0", "BMI_0", "Ethnicity_0", "Relationship_status_0",
## "Education4_0", "Income_Level_0", "Living_status_0", "Alcohol_0",
## "Smoking_Status_0", "CESD_10_0", "Anxiety_0", "Mood_Disord_0",
## "Chronic_conditions_0", "RVLT_Immediate_Normed_0", "RVLT_Delayed_Normed_0",
## "Animal_Fluency_Normed_0", "MAT_Normed_0", "RVLT_Immediate_Lang_0",
## "RVLT_Delayed_Lang_0", "MAT_Lang_0", "Animal_Fluency_Lang_0",
## "PASE_TOTAL_0", "PASE_Q1B_0", "RSTLS_Sleep_0")
Table1_Final<-CreateTableOne(vars=Baseline, data=BL.data)
print(Table1_Final,contDigits=2,missing=TRUE,quote=TRUE)
## ""
## "" "Overall" "Missing"
## "n" " 11355" " "
## "Age_0 (mean (SD))" " 61.62 (10.08)" " 0.0"
## "Sex_0 = M (%)" " 5467 (48.1) " " 0.0"
## "BMI_0 (mean (SD))" " 27.50 (5.10)" " 0.5"
## "Ethnicity_0 = White (%)" " 11043 (97.3) " " 0.0"
## "Relationship_status_0 (%)" " " " 0.0"
## " Divorced" " 997 ( 8.8) " " "
## " Married" " 8210 (72.3) " " "
## " Separated" " 290 ( 2.6) " " "
## " Single" " 852 ( 7.5) " " "
## " Widowed" " 1001 ( 8.8) " " "
## "Education4_0 (%)" " " " 0.0"
## " College Degree or Higher" " 8322 (73.3) " " "
## " High School Diploma" " 1449 (12.8) " " "
## " Less than High School Diploma" " 749 ( 6.6) " " "
## " Some College" " 835 ( 7.4) " " "
## "Income_Level_0 (%)" " " " 3.5"
## " <$20k" " 1764 (16.1) " " "
## " >$150k" " 428 ( 3.9) " " "
## " $100-150k" " 836 ( 7.6) " " "
## " $20-50k" " 4349 (39.7) " " "
## " $50-100k" " 3584 (32.7) " " "
## "Living_status_0 (%)" " " " 0.0"
## " Apartment/Condo/Townhome" " 1342 (11.8) " " "
## " Assisted Living" " 61 ( 0.5) " " "
## " House" " 9852 (86.8) " " "
## " Other" " 100 ( 0.9) " " "
## "Alcohol_0 (%)" " " " 3.1"
## " Non-drinker" " 1154 (10.5) " " "
## " Occasional drinker" " 1729 (15.7) " " "
## " Regular drinker (at least once a month)" " 8124 (73.8) " " "
## "Smoking_Status_0 (%)" " " " 0.4"
## " Daily Smoker" " 744 ( 6.6) " " "
## " Former Smoker" " 6830 (60.4) " " "
## " Never Smoked" " 3541 (31.3) " " "
## " Occasional Smoker" " 189 ( 1.7) " " "
## "CESD_10_0 (mean (SD))" " 4.96 (4.36)" " 0.3"
## "Anxiety_0 = Yes (%)" " 718 ( 6.3) " " 0.1"
## "Mood_Disord_0 = Yes (%)" " 1508 (13.3) " " 0.1"
## "Chronic_conditions_0 (mean (SD))" " 2.77 (2.26)" " 3.8"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.44 (3.81)" " 0.0"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.71 (3.71)" " 0.0"
## "Animal_Fluency_Normed_0 (mean (SD))" " 11.21 (3.43)" " 0.0"
## "MAT_Normed_0 (mean (SD))" " 9.95 (3.46)" " 0.0"
## "RVLT_Immediate_Lang_0 = French (%)" " 1962 (17.3) " " 0.0"
## "RVLT_Delayed_Lang_0 = French (%)" " 1962 (17.3) " " 0.0"
## "MAT_Lang_0 = French (%)" " 1962 (17.3) " " 0.0"
## "Animal_Fluency_Lang_0 = French (%)" " 1962 (17.3) " " 0.0"
## "PASE_TOTAL_0 (mean (SD))" "168.93 (78.96)" "19.1"
## "PASE_Q1B_0 = 1 (%)" " 4305 (38.6) " " 1.8"
## "RSTLS_Sleep_0 = 1 (%)" " 3741 (33.0) " " 0.2"
Final baseline sample stratified by whether FU2 data was collected
before (N= 6,174) or after (N= 5,181) the start of the COVID-19
pandemic
Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.data)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
## "Stratified by Pandemic"
## "" "FU2 data collected after COVID-19"
## "n" " 5181"
## "Age_0 (mean (SD))" " 60.29 (10.54)"
## "Sex_0 = M (%)" " 2856 (55.1) "
## "BMI_0 (mean (SD))" " 27.52 (4.94)"
## "Ethnicity_0 = White (%)" " 5025 (97.0) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 408 ( 7.9) "
## " Married" " 3841 (74.2) "
## " Separated" " 150 ( 2.9) "
## " Single" " 383 ( 7.4) "
## " Widowed" " 397 ( 7.7) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 3605 (69.6) "
## " High School Diploma" " 776 (15.0) "
## " Less than High School Diploma" " 411 ( 7.9) "
## " Some College" " 389 ( 7.5) "
## "Income_Level_0 (%)" " "
## " <$20k" " 783 (15.6) "
## " >$150k" " 239 ( 4.8) "
## " $100-150k" " 426 ( 8.5) "
## " $20-50k" " 1874 (37.3) "
## " $50-100k" " 1700 (33.9) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 579 (11.2) "
## " Assisted Living" " 24 ( 0.5) "
## " House" " 4541 (87.6) "
## " Other" " 37 ( 0.7) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 512 (10.2) "
## " Occasional drinker" " 784 (15.6) "
## " Regular drinker (at least once a month)" " 3731 (74.2) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 361 ( 7.0) "
## " Former Smoker" " 3128 (60.6) "
## " Never Smoked" " 1579 (30.6) "
## " Occasional Smoker" " 90 ( 1.7) "
## "CESD_10_0 (mean (SD))" " 5.08 (4.47)"
## "Anxiety_0 = Yes (%)" " 335 ( 6.5) "
## "Mood_Disord_0 = Yes (%)" " 698 (13.5) "
## "Chronic_conditions_0 (mean (SD))" " 2.60 (2.22)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.13 (3.76)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.45 (3.69)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 11.15 (3.45)"
## "MAT_Normed_0 (mean (SD))" " 9.82 (3.46)"
## "RVLT_Immediate_Lang_0 = French (%)" " 1319 (25.5) "
## "RVLT_Delayed_Lang_0 = French (%)" " 1319 (25.5) "
## "MAT_Lang_0 = French (%)" " 1319 (25.5) "
## "Animal_Fluency_Lang_0 = French (%)" " 1319 (25.5) "
## "PASE_TOTAL_0 (mean (SD))" "179.56 (81.39)"
## "PASE_Q1B_0 = 1 (%)" " 1875 (37.2) "
## "RSTLS_Sleep_0 = 1 (%)" " 1710 (33.1) "
## "Stratified by Pandemic"
## "" "FU2 data collected before COVID-19"
## "n" " 6174"
## "Age_0 (mean (SD))" " 62.74 (9.53)"
## "Sex_0 = M (%)" " 2611 (42.3) "
## "BMI_0 (mean (SD))" " 27.48 (5.24)"
## "Ethnicity_0 = White (%)" " 6018 (97.5) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 589 ( 9.5) "
## " Married" " 4369 (70.8) "
## " Separated" " 140 ( 2.3) "
## " Single" " 469 ( 7.6) "
## " Widowed" " 604 ( 9.8) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 4717 (76.4) "
## " High School Diploma" " 673 (10.9) "
## " Less than High School Diploma" " 338 ( 5.5) "
## " Some College" " 446 ( 7.2) "
## "Income_Level_0 (%)" " "
## " <$20k" " 981 (16.5) "
## " >$150k" " 189 ( 3.2) "
## " $100-150k" " 410 ( 6.9) "
## " $20-50k" " 2475 (41.7) "
## " $50-100k" " 1884 (31.7) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 763 (12.4) "
## " Assisted Living" " 37 ( 0.6) "
## " House" " 5311 (86.0) "
## " Other" " 63 ( 1.0) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 642 (10.7) "
## " Occasional drinker" " 945 (15.8) "
## " Regular drinker (at least once a month)" " 4393 (73.5) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 383 ( 6.2) "
## " Former Smoker" " 3702 (60.2) "
## " Never Smoked" " 1962 (31.9) "
## " Occasional Smoker" " 99 ( 1.6) "
## "CESD_10_0 (mean (SD))" " 4.85 (4.27)"
## "Anxiety_0 = Yes (%)" " 383 ( 6.2) "
## "Mood_Disord_0 = Yes (%)" " 810 (13.1) "
## "Chronic_conditions_0 (mean (SD))" " 2.91 (2.29)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.70 (3.84)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.93 (3.72)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 11.26 (3.41)"
## "MAT_Normed_0 (mean (SD))" " 10.06 (3.46)"
## "RVLT_Immediate_Lang_0 = French (%)" " 643 (10.4) "
## "RVLT_Delayed_Lang_0 = French (%)" " 643 (10.4) "
## "MAT_Lang_0 = French (%)" " 643 (10.4) "
## "Animal_Fluency_Lang_0 = French (%)" " 643 (10.4) "
## "PASE_TOTAL_0 (mean (SD))" "160.16 (75.79)"
## "PASE_Q1B_0 = 1 (%)" " 2430 (39.7) "
## "RSTLS_Sleep_0 = 1 (%)" " 2031 (32.9) "
## "Stratified by Pandemic"
## "" "p" "test" "Missing"
## "n" "" "" " "
## "Age_0 (mean (SD))" "<0.001" "" " 0.0"
## "Sex_0 = M (%)" "<0.001" "" " 0.0"
## "BMI_0 (mean (SD))" " 0.688" "" " 0.5"
## "Ethnicity_0 = White (%)" " 0.130" "" " 0.0"
## "Relationship_status_0 (%)" "<0.001" "" " 0.0"
## " Divorced" "" "" " "
## " Married" "" "" " "
## " Separated" "" "" " "
## " Single" "" "" " "
## " Widowed" "" "" " "
## "Education4_0 (%)" "<0.001" "" " 0.0"
## " College Degree or Higher" "" "" " "
## " High School Diploma" "" "" " "
## " Less than High School Diploma" "" "" " "
## " Some College" "" "" " "
## "Income_Level_0 (%)" "<0.001" "" " 3.5"
## " <$20k" "" "" " "
## " >$150k" "" "" " "
## " $100-150k" "" "" " "
## " $20-50k" "" "" " "
## " $50-100k" "" "" " "
## "Living_status_0 (%)" " 0.043" "" " 0.0"
## " Apartment/Condo/Townhome" "" "" " "
## " Assisted Living" "" "" " "
## " House" "" "" " "
## " Other" "" "" " "
## "Alcohol_0 (%)" " 0.584" "" " 3.1"
## " Non-drinker" "" "" " "
## " Occasional drinker" "" "" " "
## " Regular drinker (at least once a month)" "" "" " "
## "Smoking_Status_0 (%)" " 0.219" "" " 0.4"
## " Daily Smoker" "" "" " "
## " Former Smoker" "" "" " "
## " Never Smoked" "" "" " "
## " Occasional Smoker" "" "" " "
## "CESD_10_0 (mean (SD))" " 0.005" "" " 0.3"
## "Anxiety_0 = Yes (%)" " 0.589" "" " 0.1"
## "Mood_Disord_0 = Yes (%)" " 0.604" "" " 0.1"
## "Chronic_conditions_0 (mean (SD))" "<0.001" "" " 3.8"
## "RVLT_Immediate_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "RVLT_Delayed_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "Animal_Fluency_Normed_0 (mean (SD))" " 0.087" "" " 0.0"
## "MAT_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "RVLT_Immediate_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "RVLT_Delayed_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "MAT_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "Animal_Fluency_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "PASE_TOTAL_0 (mean (SD))" "<0.001" "" "19.1"
## "PASE_Q1B_0 = 1 (%)" " 0.008" "" " 1.8"
## "RSTLS_Sleep_0 = 1 (%)" " 0.903" "" " 0.2"
Stratify Results By Age, Sex, and Pandemic Status
BL.data$Age_sex<-NA
BL.data$Age_sex[BL.data$Age_0<=64 & BL.data$Sex_0 == "M"]<-"Males 45-64"
BL.data$Age_sex[BL.data$Age_0<=64 & BL.data$Sex_0 == "F"]<-"Females 45-64"
BL.data$Age_sex[BL.data$Age_0>64 & BL.data$Sex_0 == "M"]<-"Males 65+"
BL.data$Age_sex[BL.data$Age_0>64 & BL.data$Sex_0 == "F"]<-"Females 65+"
BL.MalesYoung<-subset(BL.data, Age_sex=="Males 45-64")
BL.FemalesYoung<-subset(BL.data, Age_sex=="Females 45-64")
BL.MalesOld<-subset(BL.data, Age_sex=="Males 65+")
BL.FemalesOld<-subset(BL.data, Age_sex=="Females 65+")
Males 45-64
Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.MalesYoung)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
## "Stratified by Pandemic"
## "" "FU2 data collected after COVID-19"
## "n" " 2032"
## "Age_0 (mean (SD))" " 54.12 (5.36)"
## "Sex_0 = M (%)" " 2032 (100.0) "
## "BMI_0 (mean (SD))" " 27.98 (4.44)"
## "Ethnicity_0 = White (%)" " 1947 ( 95.8) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 126 ( 6.2) "
## " Married" " 1656 ( 81.5) "
## " Separated" " 64 ( 3.2) "
## " Single" " 163 ( 8.0) "
## " Widowed" " 22 ( 1.1) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 1555 ( 76.5) "
## " High School Diploma" " 248 ( 12.2) "
## " Less than High School Diploma" " 95 ( 4.7) "
## " Some College" " 134 ( 6.6) "
## "Income_Level_0 (%)" " "
## " <$20k" " 123 ( 6.2) "
## " >$150k" " 188 ( 9.4) "
## " $100-150k" " 297 ( 14.9) "
## " $20-50k" " 517 ( 25.9) "
## " $50-100k" " 873 ( 43.7) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 146 ( 7.2) "
## " Assisted Living" " 2 ( 0.1) "
## " House" " 1872 ( 92.1) "
## " Other" " 12 ( 0.6) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 174 ( 8.7) "
## " Occasional drinker" " 223 ( 11.2) "
## " Regular drinker (at least once a month)" " 1597 ( 80.1) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 168 ( 8.3) "
## " Former Smoker" " 1211 ( 59.8) "
## " Never Smoked" " 605 ( 29.9) "
## " Occasional Smoker" " 40 ( 2.0) "
## "CESD_10_0 (mean (SD))" " 4.82 (4.40)"
## "Anxiety_0 = Yes (%)" " 100 ( 4.9) "
## "Mood_Disord_0 = Yes (%)" " 257 ( 12.7) "
## "Chronic_conditions_0 (mean (SD))" " 1.91 (1.76)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 9.73 (3.52)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.02 (3.56)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 11.24 (3.55)"
## "MAT_Normed_0 (mean (SD))" " 9.75 (3.41)"
## "RVLT_Immediate_Lang_0 = French (%)" " 485 ( 23.9) "
## "RVLT_Delayed_Lang_0 = French (%)" " 485 ( 23.9) "
## "MAT_Lang_0 = French (%)" " 485 ( 23.9) "
## "Animal_Fluency_Lang_0 = French (%)" " 485 ( 23.9) "
## "PASE_TOTAL_0 (mean (SD))" "217.41 (81.01)"
## "PASE_Q1B_0 = 1 (%)" " 630 ( 31.9) "
## "RSTLS_Sleep_0 = 1 (%)" " 634 ( 31.2) "
## "Stratified by Pandemic"
## "" "FU2 data collected before COVID-19"
## "n" " 1424"
## "Age_0 (mean (SD))" " 56.68 (5.24)"
## "Sex_0 = M (%)" " 1424 (100.0) "
## "BMI_0 (mean (SD))" " 28.10 (4.65)"
## "Ethnicity_0 = White (%)" " 1381 ( 97.0) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 73 ( 5.1) "
## " Married" " 1152 ( 81.0) "
## " Separated" " 43 ( 3.0) "
## " Single" " 127 ( 8.9) "
## " Widowed" " 28 ( 2.0) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 1125 ( 79.0) "
## " High School Diploma" " 147 ( 10.3) "
## " Less than High School Diploma" " 50 ( 3.5) "
## " Some College" " 102 ( 7.2) "
## "Income_Level_0 (%)" " "
## " <$20k" " 94 ( 6.8) "
## " >$150k" " 92 ( 6.6) "
## " $100-150k" " 207 ( 14.9) "
## " $20-50k" " 414 ( 29.7) "
## " $50-100k" " 585 ( 42.0) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 122 ( 8.6) "
## " Assisted Living" " 1 ( 0.1) "
## " House" " 1290 ( 90.6) "
## " Other" " 11 ( 0.8) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 131 ( 9.3) "
## " Occasional drinker" " 136 ( 9.7) "
## " Regular drinker (at least once a month)" " 1135 ( 81.0) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 105 ( 7.4) "
## " Former Smoker" " 874 ( 61.7) "
## " Never Smoked" " 401 ( 28.3) "
## " Occasional Smoker" " 37 ( 2.6) "
## "CESD_10_0 (mean (SD))" " 4.51 (4.01)"
## "Anxiety_0 = Yes (%)" " 68 ( 4.8) "
## "Mood_Disord_0 = Yes (%)" " 144 ( 10.1) "
## "Chronic_conditions_0 (mean (SD))" " 2.11 (1.75)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.40 (3.86)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.60 (3.77)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 11.31 (3.67)"
## "MAT_Normed_0 (mean (SD))" " 10.02 (3.38)"
## "RVLT_Immediate_Lang_0 = French (%)" " 147 ( 10.3) "
## "RVLT_Delayed_Lang_0 = French (%)" " 147 ( 10.3) "
## "MAT_Lang_0 = French (%)" " 147 ( 10.3) "
## "Animal_Fluency_Lang_0 = French (%)" " 147 ( 10.3) "
## "PASE_TOTAL_0 (mean (SD))" "199.82 (78.37)"
## "PASE_Q1B_0 = 1 (%)" " 510 ( 36.3) "
## "RSTLS_Sleep_0 = 1 (%)" " 422 ( 29.7) "
## "Stratified by Pandemic"
## "" "p" "test" "Missing"
## "n" "" "" " "
## "Age_0 (mean (SD))" "<0.001" "" " 0.0"
## "Sex_0 = M (%)" " NA" "" " 0.0"
## "BMI_0 (mean (SD))" " 0.437" "" " 0.2"
## "Ethnicity_0 = White (%)" " 0.091" "" " 0.0"
## "Relationship_status_0 (%)" " 0.132" "" " 0.1"
## " Divorced" "" "" " "
## " Married" "" "" " "
## " Separated" "" "" " "
## " Single" "" "" " "
## " Widowed" "" "" " "
## "Education4_0 (%)" " 0.096" "" " 0.0"
## " College Degree or Higher" "" "" " "
## " High School Diploma" "" "" " "
## " Less than High School Diploma" "" "" " "
## " Some College" "" "" " "
## "Income_Level_0 (%)" " 0.010" "" " 1.9"
## " <$20k" "" "" " "
## " >$150k" "" "" " "
## " $100-150k" "" "" " "
## " $20-50k" "" "" " "
## " $50-100k" "" "" " "
## "Living_status_0 (%)" " 0.428" "" " 0.0"
## " Apartment/Condo/Townhome" "" "" " "
## " Assisted Living" "" "" " "
## " House" "" "" " "
## " Other" "" "" " "
## "Alcohol_0 (%)" " 0.343" "" " 1.7"
## " Non-drinker" "" "" " "
## " Occasional drinker" "" "" " "
## " Regular drinker (at least once a month)" "" "" " "
## "Smoking_Status_0 (%)" " 0.317" "" " 0.4"
## " Daily Smoker" "" "" " "
## " Former Smoker" "" "" " "
## " Never Smoked" "" "" " "
## " Occasional Smoker" "" "" " "
## "CESD_10_0 (mean (SD))" " 0.032" "" " 0.2"
## "Anxiety_0 = Yes (%)" " 0.905" "" " 0.0"
## "Mood_Disord_0 = Yes (%)" " 0.026" "" " 0.1"
## "Chronic_conditions_0 (mean (SD))" " 0.001" "" " 2.7"
## "RVLT_Immediate_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "RVLT_Delayed_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "Animal_Fluency_Normed_0 (mean (SD))" " 0.576" "" " 0.0"
## "MAT_Normed_0 (mean (SD))" " 0.022" "" " 0.0"
## "RVLT_Immediate_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "RVLT_Delayed_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "MAT_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "Animal_Fluency_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "PASE_TOTAL_0 (mean (SD))" "<0.001" "" "17.5"
## "PASE_Q1B_0 = 1 (%)" " 0.008" "" " 2.3"
## "RSTLS_Sleep_0 = 1 (%)" " 0.348" "" " 0.1"
Females 45-64
Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.FemalesYoung)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
## "Stratified by Pandemic"
## "" "FU2 data collected after COVID-19"
## "n" " 1498"
## "Age_0 (mean (SD))" " 54.09 (5.34)"
## "Sex_0 = F (%)" " 1498 (100.0) "
## "BMI_0 (mean (SD))" " 27.18 (5.75)"
## "Ethnicity_0 = White (%)" " 1460 ( 97.5) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 137 ( 9.1) "
## " Married" " 1107 ( 73.9) "
## " Separated" " 56 ( 3.7) "
## " Single" " 141 ( 9.4) "
## " Widowed" " 57 ( 3.8) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 1064 ( 71.0) "
## " High School Diploma" " 248 ( 16.6) "
## " Less than High School Diploma" " 65 ( 4.3) "
## " Some College" " 121 ( 8.1) "
## "Income_Level_0 (%)" " "
## " <$20k" " 320 ( 22.2) "
## " >$150k" " 26 ( 1.8) "
## " $100-150k" " 71 ( 4.9) "
## " $20-50k" " 550 ( 38.2) "
## " $50-100k" " 473 ( 32.8) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 119 ( 7.9) "
## " Assisted Living" " 1 ( 0.1) "
## " House" " 1371 ( 91.5) "
## " Other" " 7 ( 0.5) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 140 ( 9.6) "
## " Occasional drinker" " 283 ( 19.4) "
## " Regular drinker (at least once a month)" " 1039 ( 71.1) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 138 ( 9.3) "
## " Former Smoker" " 838 ( 56.2) "
## " Never Smoked" " 479 ( 32.1) "
## " Occasional Smoker" " 36 ( 2.4) "
## "CESD_10_0 (mean (SD))" " 5.68 (4.91)"
## "Anxiety_0 = Yes (%)" " 159 ( 10.6) "
## "Mood_Disord_0 = Yes (%)" " 298 ( 19.9) "
## "Chronic_conditions_0 (mean (SD))" " 2.36 (2.05)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.13 (3.67)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.31 (3.50)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 11.21 (3.46)"
## "MAT_Normed_0 (mean (SD))" " 9.92 (3.32)"
## "RVLT_Immediate_Lang_0 = French (%)" " 396 ( 26.4) "
## "RVLT_Delayed_Lang_0 = French (%)" " 396 ( 26.4) "
## "MAT_Lang_0 = French (%)" " 396 ( 26.4) "
## "Animal_Fluency_Lang_0 = French (%)" " 396 ( 26.4) "
## "PASE_TOTAL_0 (mean (SD))" "182.11 (73.93)"
## "PASE_Q1B_0 = 1 (%)" " 507 ( 35.1) "
## "RSTLS_Sleep_0 = 1 (%)" " 582 ( 39.0) "
## "Stratified by Pandemic"
## "" "FU2 data collected before COVID-19"
## "n" " 2246"
## "Age_0 (mean (SD))" " 55.97 (5.32)"
## "Sex_0 = F (%)" " 2246 (100.0) "
## "BMI_0 (mean (SD))" " 27.52 (6.16)"
## "Ethnicity_0 = White (%)" " 2188 ( 97.4) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 250 ( 11.1) "
## " Married" " 1620 ( 72.1) "
## " Separated" " 62 ( 2.8) "
## " Single" " 213 ( 9.5) "
## " Widowed" " 101 ( 4.5) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 1747 ( 77.8) "
## " High School Diploma" " 244 ( 10.9) "
## " Less than High School Diploma" " 82 ( 3.7) "
## " Some College" " 173 ( 7.7) "
## "Income_Level_0 (%)" " "
## " <$20k" " 483 ( 22.3) "
## " >$150k" " 41 ( 1.9) "
## " $100-150k" " 113 ( 5.2) "
## " $20-50k" " 847 ( 39.1) "
## " $50-100k" " 680 ( 31.4) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 217 ( 9.7) "
## " Assisted Living" " 4 ( 0.2) "
## " House" " 2006 ( 89.3) "
## " Other" " 19 ( 0.8) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 224 ( 10.2) "
## " Occasional drinker" " 422 ( 19.3) "
## " Regular drinker (at least once a month)" " 1546 ( 70.5) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 187 ( 8.3) "
## " Former Smoker" " 1200 ( 53.5) "
## " Never Smoked" " 810 ( 36.1) "
## " Occasional Smoker" " 45 ( 2.0) "
## "CESD_10_0 (mean (SD))" " 5.26 (4.56)"
## "Anxiety_0 = Yes (%)" " 180 ( 8.0) "
## "Mood_Disord_0 = Yes (%)" " 423 ( 18.8) "
## "Chronic_conditions_0 (mean (SD))" " 2.59 (2.14)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.65 (3.81)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.85 (3.61)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 11.36 (3.46)"
## "MAT_Normed_0 (mean (SD))" " 10.16 (3.38)"
## "RVLT_Immediate_Lang_0 = French (%)" " 304 ( 13.5) "
## "RVLT_Delayed_Lang_0 = French (%)" " 304 ( 13.5) "
## "MAT_Lang_0 = French (%)" " 304 ( 13.5) "
## "Animal_Fluency_Lang_0 = French (%)" " 304 ( 13.5) "
## "PASE_TOTAL_0 (mean (SD))" "170.96 (72.48)"
## "PASE_Q1B_0 = 1 (%)" " 830 ( 37.3) "
## "RSTLS_Sleep_0 = 1 (%)" " 829 ( 37.0) "
## "Stratified by Pandemic"
## "" "p" "test" "Missing"
## "n" "" "" " "
## "Age_0 (mean (SD))" "<0.001" "" " 0.0"
## "Sex_0 = F (%)" " NA" "" " 0.0"
## "BMI_0 (mean (SD))" " 0.084" "" " 0.9"
## "Ethnicity_0 = White (%)" " 1.000" "" " 0.0"
## "Relationship_status_0 (%)" " 0.109" "" " 0.0"
## " Divorced" "" "" " "
## " Married" "" "" " "
## " Separated" "" "" " "
## " Single" "" "" " "
## " Widowed" "" "" " "
## "Education4_0 (%)" "<0.001" "" " 0.0"
## " College Degree or Higher" "" "" " "
## " High School Diploma" "" "" " "
## " Less than High School Diploma" "" "" " "
## " Some College" "" "" " "
## "Income_Level_0 (%)" " 0.920" "" " 3.7"
## " <$20k" "" "" " "
## " >$150k" "" "" " "
## " $100-150k" "" "" " "
## " $20-50k" "" "" " "
## " $50-100k" "" "" " "
## "Living_status_0 (%)" " 0.105" "" " 0.0"
## " Apartment/Condo/Townhome" "" "" " "
## " Assisted Living" "" "" " "
## " House" "" "" " "
## " Other" "" "" " "
## "Alcohol_0 (%)" " 0.817" "" " 2.4"
## " Non-drinker" "" "" " "
## " Occasional drinker" "" "" " "
## " Regular drinker (at least once a month)" "" "" " "
## "Smoking_Status_0 (%)" " 0.076" "" " 0.3"
## " Daily Smoker" "" "" " "
## " Former Smoker" "" "" " "
## " Never Smoked" "" "" " "
## " Occasional Smoker" "" "" " "
## "CESD_10_0 (mean (SD))" " 0.008" "" " 0.2"
## "Anxiety_0 = Yes (%)" " 0.007" "" " 0.1"
## "Mood_Disord_0 = Yes (%)" " 0.443" "" " 0.1"
## "Chronic_conditions_0 (mean (SD))" " 0.001" "" " 3.3"
## "RVLT_Immediate_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "RVLT_Delayed_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "Animal_Fluency_Normed_0 (mean (SD))" " 0.204" "" " 0.0"
## "MAT_Normed_0 (mean (SD))" " 0.029" "" " 0.0"
## "RVLT_Immediate_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "RVLT_Delayed_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "MAT_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "Animal_Fluency_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "PASE_TOTAL_0 (mean (SD))" "<0.001" "" "18.8"
## "PASE_Q1B_0 = 1 (%)" " 0.189" "" " 2.0"
## "RSTLS_Sleep_0 = 1 (%)" " 0.231" "" " 0.2"
Males 65+
Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.MalesOld)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
## "Stratified by Pandemic"
## "" "FU2 data collected after COVID-19"
## "n" " 824"
## "Age_0 (mean (SD))" " 73.50 (5.55)"
## "Sex_0 = M (%)" " 824 (100.0) "
## "BMI_0 (mean (SD))" " 27.23 (4.10)"
## "Ethnicity_0 = White (%)" " 802 ( 97.3) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 47 ( 5.7) "
## " Married" " 663 ( 80.6) "
## " Separated" " 18 ( 2.2) "
## " Single" " 33 ( 4.0) "
## " Widowed" " 62 ( 7.5) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 542 ( 65.8) "
## " High School Diploma" " 113 ( 13.7) "
## " Less than High School Diploma" " 103 ( 12.5) "
## " Some College" " 66 ( 8.0) "
## "Income_Level_0 (%)" " "
## " <$20k" " 79 ( 9.8) "
## " >$150k" " 19 ( 2.4) "
## " $100-150k" " 48 ( 6.0) "
## " $20-50k" " 408 ( 50.7) "
## " $50-100k" " 250 ( 31.1) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 125 ( 15.2) "
## " Assisted Living" " 4 ( 0.5) "
## " House" " 690 ( 83.7) "
## " Other" " 5 ( 0.6) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 89 ( 11.1) "
## " Occasional drinker" " 92 ( 11.5) "
## " Regular drinker (at least once a month)" " 618 ( 77.3) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 22 ( 2.7) "
## " Former Smoker" " 620 ( 75.7) "
## " Never Smoked" " 171 ( 20.9) "
## " Occasional Smoker" " 6 ( 0.7) "
## "CESD_10_0 (mean (SD))" " 4.39 (3.83)"
## "Anxiety_0 = Yes (%)" " 22 ( 2.7) "
## "Mood_Disord_0 = Yes (%)" " 46 ( 5.6) "
## "Chronic_conditions_0 (mean (SD))" " 3.24 (2.21)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.32 (4.11)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.83 (3.91)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 10.99 (3.26)"
## "MAT_Normed_0 (mean (SD))" " 9.69 (3.57)"
## "RVLT_Immediate_Lang_0 = French (%)" " 220 ( 26.7) "
## "RVLT_Delayed_Lang_0 = French (%)" " 220 ( 26.7) "
## "MAT_Lang_0 = French (%)" " 220 ( 26.7) "
## "Animal_Fluency_Lang_0 = French (%)" " 220 ( 26.7) "
## "PASE_TOTAL_0 (mean (SD))" "138.16 (60.96)"
## "PASE_Q1B_0 = 1 (%)" " 355 ( 44.3) "
## "RSTLS_Sleep_0 = 1 (%)" " 232 ( 28.3) "
## "Stratified by Pandemic"
## "" "FU2 data collected before COVID-19"
## "n" " 1187"
## "Age_0 (mean (SD))" " 72.09 (5.35)"
## "Sex_0 = M (%)" " 1187 (100.0) "
## "BMI_0 (mean (SD))" " 27.19 (3.85)"
## "Ethnicity_0 = White (%)" " 1156 ( 97.4) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 73 ( 6.1) "
## " Married" " 937 ( 78.9) "
## " Separated" " 16 ( 1.3) "
## " Single" " 50 ( 4.2) "
## " Widowed" " 111 ( 9.4) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 870 ( 73.3) "
## " High School Diploma" " 125 ( 10.5) "
## " Less than High School Diploma" " 100 ( 8.4) "
## " Some College" " 92 ( 7.8) "
## "Income_Level_0 (%)" " "
## " <$20k" " 89 ( 7.7) "
## " >$150k" " 47 ( 4.1) "
## " $100-150k" " 71 ( 6.1) "
## " $20-50k" " 547 ( 47.3) "
## " $50-100k" " 403 ( 34.8) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 152 ( 12.8) "
## " Assisted Living" " 9 ( 0.8) "
## " House" " 1009 ( 85.0) "
## " Other" " 17 ( 1.4) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 124 ( 10.8) "
## " Occasional drinker" " 118 ( 10.2) "
## " Regular drinker (at least once a month)" " 911 ( 79.0) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 39 ( 3.3) "
## " Former Smoker" " 865 ( 73.4) "
## " Never Smoked" " 267 ( 22.7) "
## " Occasional Smoker" " 7 ( 0.6) "
## "CESD_10_0 (mean (SD))" " 4.17 (3.69)"
## "Anxiety_0 = Yes (%)" " 45 ( 3.8) "
## "Mood_Disord_0 = Yes (%)" " 87 ( 7.3) "
## "Chronic_conditions_0 (mean (SD))" " 3.32 (2.30)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.62 (3.68)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.91 (3.67)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 11.07 (3.24)"
## "MAT_Normed_0 (mean (SD))" " 10.04 (3.53)"
## "RVLT_Immediate_Lang_0 = French (%)" " 88 ( 7.4) "
## "RVLT_Delayed_Lang_0 = French (%)" " 88 ( 7.4) "
## "MAT_Lang_0 = French (%)" " 88 ( 7.4) "
## "Animal_Fluency_Lang_0 = French (%)" " 88 ( 7.4) "
## "PASE_TOTAL_0 (mean (SD))" "136.93 (63.79)"
## "PASE_Q1B_0 = 1 (%)" " 541 ( 45.8) "
## "RSTLS_Sleep_0 = 1 (%)" " 345 ( 29.1) "
## "Stratified by Pandemic"
## "" "p" "test" "Missing"
## "n" "" "" " "
## "Age_0 (mean (SD))" "<0.001" "" " 0.0"
## "Sex_0 = M (%)" " NA" "" " 0.0"
## "BMI_0 (mean (SD))" " 0.834" "" " 0.2"
## "Ethnicity_0 = White (%)" " 1.000" "" " 0.0"
## "Relationship_status_0 (%)" " 0.373" "" " 0.0"
## " Divorced" "" "" " "
## " Married" "" "" " "
## " Separated" "" "" " "
## " Single" "" "" " "
## " Widowed" "" "" " "
## "Education4_0 (%)" " 0.001" "" " 0.0"
## " College Degree or Higher" "" "" " "
## " High School Diploma" "" "" " "
## " Less than High School Diploma" "" "" " "
## " Some College" "" "" " "
## "Income_Level_0 (%)" " 0.044" "" " 2.5"
## " <$20k" "" "" " "
## " >$150k" "" "" " "
## " $100-150k" "" "" " "
## " $20-50k" "" "" " "
## " $50-100k" "" "" " "
## "Living_status_0 (%)" " 0.130" "" " 0.0"
## " Apartment/Condo/Townhome" "" "" " "
## " Assisted Living" "" "" " "
## " House" "" "" " "
## " Other" "" "" " "
## "Alcohol_0 (%)" " 0.622" "" " 2.9"
## " Non-drinker" "" "" " "
## " Occasional drinker" "" "" " "
## " Regular drinker (at least once a month)" "" "" " "
## "Smoking_Status_0 (%)" " 0.616" "" " 0.7"
## " Daily Smoker" "" "" " "
## " Former Smoker" "" "" " "
## " Never Smoked" "" "" " "
## " Occasional Smoker" "" "" " "
## "CESD_10_0 (mean (SD))" " 0.199" "" " 0.5"
## "Anxiety_0 = Yes (%)" " 0.208" "" " 0.1"
## "Mood_Disord_0 = Yes (%)" " 0.145" "" " 0.1"
## "Chronic_conditions_0 (mean (SD))" " 0.475" "" " 4.6"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 0.083" "" " 0.0"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 0.609" "" " 0.0"
## "Animal_Fluency_Normed_0 (mean (SD))" " 0.574" "" " 0.0"
## "MAT_Normed_0 (mean (SD))" " 0.032" "" " 0.0"
## "RVLT_Immediate_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "RVLT_Delayed_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "MAT_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "Animal_Fluency_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "PASE_TOTAL_0 (mean (SD))" " 0.694" "" "18.3"
## "PASE_Q1B_0 = 1 (%)" " 0.516" "" " 1.4"
## "RSTLS_Sleep_0 = 1 (%)" " 0.718" "" " 0.3"
Females 65+
Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.FemalesOld)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
## "Stratified by Pandemic"
## "" "FU2 data collected after COVID-19"
## "n" " 827"
## "Age_0 (mean (SD))" " 73.52 (5.56)"
## "Sex_0 = F (%)" " 827 (100.0) "
## "BMI_0 (mean (SD))" " 27.31 (5.19)"
## "Ethnicity_0 = White (%)" " 816 ( 98.7) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 98 ( 11.9) "
## " Married" " 415 ( 50.2) "
## " Separated" " 12 ( 1.5) "
## " Single" " 46 ( 5.6) "
## " Widowed" " 256 ( 31.0) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 444 ( 53.7) "
## " High School Diploma" " 167 ( 20.2) "
## " Less than High School Diploma" " 148 ( 17.9) "
## " Some College" " 68 ( 8.2) "
## "Income_Level_0 (%)" " "
## " <$20k" " 261 ( 33.5) "
## " >$150k" " 6 ( 0.8) "
## " $100-150k" " 10 ( 1.3) "
## " $20-50k" " 399 ( 51.2) "
## " $50-100k" " 104 ( 13.3) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 189 ( 22.9) "
## " Assisted Living" " 17 ( 2.1) "
## " House" " 608 ( 73.5) "
## " Other" " 13 ( 1.6) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 109 ( 14.1) "
## " Occasional drinker" " 186 ( 24.1) "
## " Regular drinker (at least once a month)" " 477 ( 61.8) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 33 ( 4.0) "
## " Former Smoker" " 459 ( 55.7) "
## " Never Smoked" " 324 ( 39.3) "
## " Occasional Smoker" " 8 ( 1.0) "
## "CESD_10_0 (mean (SD))" " 5.33 (4.22)"
## "Anxiety_0 = Yes (%)" " 54 ( 6.5) "
## "Mood_Disord_0 = Yes (%)" " 97 ( 11.7) "
## "Chronic_conditions_0 (mean (SD))" " 4.16 (2.60)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.92 (3.97)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 11.36 (3.91)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 10.96 (3.36)"
## "MAT_Normed_0 (mean (SD))" " 9.94 (3.71)"
## "RVLT_Immediate_Lang_0 = French (%)" " 218 ( 26.4) "
## "RVLT_Delayed_Lang_0 = French (%)" " 218 ( 26.4) "
## "MAT_Lang_0 = French (%)" " 218 ( 26.4) "
## "Animal_Fluency_Lang_0 = French (%)" " 218 ( 26.4) "
## "PASE_TOTAL_0 (mean (SD))" "119.62 (54.01)"
## "PASE_Q1B_0 = 1 (%)" " 383 ( 47.0) "
## "RSTLS_Sleep_0 = 1 (%)" " 262 ( 31.7) "
## "Stratified by Pandemic"
## "" "FU2 data collected before COVID-19"
## "n" " 1317"
## "Age_0 (mean (SD))" " 72.42 (5.62)"
## "Sex_0 = F (%)" " 1317 (100.0) "
## "BMI_0 (mean (SD))" " 27.00 (5.16)"
## "Ethnicity_0 = White (%)" " 1293 ( 98.2) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 193 ( 14.7) "
## " Married" " 660 ( 50.2) "
## " Separated" " 19 ( 1.4) "
## " Single" " 79 ( 6.0) "
## " Widowed" " 364 ( 27.7) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 975 ( 74.0) "
## " High School Diploma" " 157 ( 11.9) "
## " Less than High School Diploma" " 106 ( 8.0) "
## " Some College" " 79 ( 6.0) "
## "Income_Level_0 (%)" " "
## " <$20k" " 315 ( 25.7) "
## " >$150k" " 9 ( 0.7) "
## " $100-150k" " 19 ( 1.5) "
## " $20-50k" " 667 ( 54.4) "
## " $50-100k" " 216 ( 17.6) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 272 ( 20.7) "
## " Assisted Living" " 23 ( 1.7) "
## " House" " 1006 ( 76.4) "
## " Other" " 16 ( 1.2) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 163 ( 13.2) "
## " Occasional drinker" " 269 ( 21.8) "
## " Regular drinker (at least once a month)" " 801 ( 65.0) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 52 ( 4.0) "
## " Former Smoker" " 763 ( 58.3) "
## " Never Smoked" " 484 ( 37.0) "
## " Occasional Smoker" " 10 ( 0.8) "
## "CESD_10_0 (mean (SD))" " 5.13 (4.43)"
## "Anxiety_0 = Yes (%)" " 90 ( 6.8) "
## "Mood_Disord_0 = Yes (%)" " 156 ( 11.9) "
## "Chronic_conditions_0 (mean (SD))" " 3.99 (2.59)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 11.18 (3.99)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 11.45 (3.84)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 11.20 (3.18)"
## "MAT_Normed_0 (mean (SD))" " 9.97 (3.60)"
## "RVLT_Immediate_Lang_0 = French (%)" " 104 ( 7.9) "
## "RVLT_Delayed_Lang_0 = French (%)" " 104 ( 7.9) "
## "MAT_Lang_0 = French (%)" " 104 ( 7.9) "
## "Animal_Fluency_Lang_0 = French (%)" " 104 ( 7.9) "
## "PASE_TOTAL_0 (mean (SD))" "115.93 (56.88)"
## "PASE_Q1B_0 = 1 (%)" " 549 ( 41.9) "
## "RSTLS_Sleep_0 = 1 (%)" " 435 ( 33.1) "
## "Stratified by Pandemic"
## "" "p" "test" "Missing"
## "n" "" "" " "
## "Age_0 (mean (SD))" "<0.001" "" " 0.0"
## "Sex_0 = F (%)" " NA" "" " 0.0"
## "BMI_0 (mean (SD))" " 0.184" "" " 0.7"
## "Ethnicity_0 = White (%)" " 0.484" "" " 0.0"
## "Relationship_status_0 (%)" " 0.283" "" " 0.1"
## " Divorced" "" "" " "
## " Married" "" "" " "
## " Separated" "" "" " "
## " Single" "" "" " "
## " Widowed" "" "" " "
## "Education4_0 (%)" "<0.001" "" " 0.0"
## " College Degree or Higher" "" "" " "
## " High School Diploma" "" "" " "
## " Less than High School Diploma" "" "" " "
## " Some College" "" "" " "
## "Income_Level_0 (%)" " 0.002" "" " 6.4"
## " <$20k" "" "" " "
## " >$150k" "" "" " "
## " $100-150k" "" "" " "
## " $20-50k" "" "" " "
## " $50-100k" "" "" " "
## "Living_status_0 (%)" " 0.487" "" " 0.0"
## " Apartment/Condo/Townhome" "" "" " "
## " Assisted Living" "" "" " "
## " House" "" "" " "
## " Other" "" "" " "
## "Alcohol_0 (%)" " 0.347" "" " 6.5"
## " Non-drinker" "" "" " "
## " Occasional drinker" "" "" " "
## " Regular drinker (at least once a month)" "" "" " "
## "Smoking_Status_0 (%)" " 0.663" "" " 0.5"
## " Daily Smoker" "" "" " "
## " Former Smoker" "" "" " "
## " Never Smoked" "" "" " "
## " Occasional Smoker" "" "" " "
## "CESD_10_0 (mean (SD))" " 0.294" "" " 0.4"
## "Anxiety_0 = Yes (%)" " 0.855" "" " 0.1"
## "Mood_Disord_0 = Yes (%)" " 0.985" "" " 0.0"
## "Chronic_conditions_0 (mean (SD))" " 0.166" "" " 5.5"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 0.140" "" " 0.0"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 0.589" "" " 0.0"
## "Animal_Fluency_Normed_0 (mean (SD))" " 0.103" "" " 0.0"
## "MAT_Normed_0 (mean (SD))" " 0.837" "" " 0.0"
## "RVLT_Immediate_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "RVLT_Delayed_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "MAT_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "Animal_Fluency_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "PASE_TOTAL_0 (mean (SD))" " 0.193" "" "23.2"
## "PASE_Q1B_0 = 1 (%)" " 0.025" "" " 0.9"
## "RSTLS_Sleep_0 = 1 (%)" " 0.544" "" " 0.1"
5) Main Effects Model
All models use normalized cognitive scores. Each model is adjusted
for baseline age, sex, education, ethnicity, income level, baseline BMI,
baseline CESD-10 score, smoking status, relationship status at baseline,
living status at baseline, diagnosis of anxiety or mood disorder at
baseline, number of chronic conditions at baseline, baseline PASE score,
and baseline cognitive performance
Contrast statements
#Contrast 1: Group differences from FU1 to FU2 (for significant effects)
c1=matrix(c(0,1,0,-1))
c2=matrix(c(1,0,-1,0))
c1st=c1 - c2
#Contrast 2: 65+ males and group differences from FU1 to FU2 (for significant effects)
c3=matrix(c(0,0,0,0,0,0,0,0,0,0,0,0,1,0,-1,0))
c4=matrix(c(0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,-1))
c2nd=c3 - c4
#Contrast 3: 65+ females and group differences from FU1 to FU2 (for significant effects)
c5=matrix(c(0,0,0,0,1,0,-1,0,0,0,0,0,0,0,0,0))
c6=matrix(c(0,0,0,0,0,1,0,-1,0,0,0,0,0,0,0,0))
c3rd=c5 - c6
#Contrast 3: 45-64 females and group differences from FU1 to FU2 (for significant effects)
c7=matrix(c(1,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0))
c8=matrix(c(0,1,0,-1,0,0,0,0,0,0,0,0,0,0,0,0))
c4th=c7 - c8
emm_options(opt.digits = FALSE)
emm_options(pbkrtest.limit = 50000)
set.seed(1)
5.1) RVLT Immediate Recall
5.1.1) Model
modelRVLT_imm_adj10<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + RVLT_Immediate_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelRVLT_imm_adj10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + RVLT_Immediate_Normedbaseline + (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 105600.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6632 -0.5628 -0.0352 0.5307 3.8380
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.154 2.038
## Residual 7.456 2.731
## Number of obs: 20202, groups: ID, 10396
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 8.270e+00 3.554e-01
## timefactor2 3.271e-01 5.737e-02
## PandemicFU2 data collected before COVID-19 1.812e-01 6.889e-02
## Age -2.367e-02 3.395e-03
## SexM -5.478e-01 6.209e-02
## EducationHigh School Diploma 2.302e-01 8.733e-02
## EducationLess than High School Diploma 5.084e-01 1.207e-01
## EducationSome College 1.737e-01 1.090e-01
## EthnicityWhite 6.505e-01 1.713e-01
## IncomeLevel>$150k 7.030e-01 1.644e-01
## IncomeLevel$100-150k 5.749e-01 1.308e-01
## IncomeLevel$20-50k 2.324e-01 8.533e-02
## IncomeLevel$50-100k 5.311e-01 9.238e-02
## BMI -1.667e-02 5.664e-03
## CESD.10baseline -2.056e-02 6.930e-03
## SmokingStatusFormer Smoker 7.653e-02 1.169e-01
## SmokingStatusNever Smoked 2.201e-01 1.220e-01
## SmokingStatusOccasional Smoker -1.183e-01 2.385e-01
## RelationshipstatusMarried 2.324e-01 1.028e-01
## RelationshipstatusSeparated 1.104e-01 2.009e-01
## RelationshipstatusSingle 5.907e-02 1.391e-01
## RelationshipstatusWidowed -5.266e-02 1.381e-01
## LivingstatusAssisted Living -1.093e+00 4.033e-01
## LivingstatusHouse 7.423e-02 9.177e-02
## LivingstatusOther 3.873e-02 3.145e-01
## AnxietyYes -8.563e-03 1.209e-01
## MoodDisordYes -1.435e-01 8.828e-02
## Chronicconditions -2.969e-02 1.411e-02
## RVLT_Immediate_Normedbaseline 3.479e-01 7.409e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -9.844e-02 7.759e-02
## df t value
## (Intercept) 1.044e+04 23.267
## timefactor2 1.019e+04 5.702
## PandemicFU2 data collected before COVID-19 1.783e+04 2.630
## Age 1.033e+04 -6.972
## SexM 1.031e+04 -8.822
## EducationHigh School Diploma 1.033e+04 2.636
## EducationLess than High School Diploma 1.045e+04 4.211
## EducationSome College 1.022e+04 1.593
## EthnicityWhite 1.026e+04 3.796
## IncomeLevel>$150k 1.031e+04 4.275
## IncomeLevel$100-150k 1.027e+04 4.396
## IncomeLevel$20-50k 1.032e+04 2.724
## IncomeLevel$50-100k 1.031e+04 5.749
## BMI 1.026e+04 -2.943
## CESD.10baseline 1.032e+04 -2.967
## SmokingStatusFormer Smoker 1.038e+04 0.655
## SmokingStatusNever Smoked 1.037e+04 1.804
## SmokingStatusOccasional Smoker 1.026e+04 -0.496
## RelationshipstatusMarried 1.034e+04 2.259
## RelationshipstatusSeparated 1.040e+04 0.549
## RelationshipstatusSingle 1.033e+04 0.425
## RelationshipstatusWidowed 1.034e+04 -0.381
## LivingstatusAssisted Living 1.031e+04 -2.711
## LivingstatusHouse 1.032e+04 0.809
## LivingstatusOther 1.021e+04 0.123
## AnxietyYes 1.031e+04 -0.071
## MoodDisordYes 1.031e+04 -1.625
## Chronicconditions 1.031e+04 -2.105
## RVLT_Immediate_Normedbaseline 1.033e+04 46.952
## timefactor2:PandemicFU2 data collected before COVID-19 1.013e+04 -1.269
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 1.22e-08 ***
## PandemicFU2 data collected before COVID-19 0.008537 **
## Age 3.33e-12 ***
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.008404 **
## EducationLess than High School Diploma 2.56e-05 ***
## EducationSome College 0.111152
## EthnicityWhite 0.000148 ***
## IncomeLevel>$150k 1.93e-05 ***
## IncomeLevel$100-150k 1.11e-05 ***
## IncomeLevel$20-50k 0.006470 **
## IncomeLevel$50-100k 9.22e-09 ***
## BMI 0.003253 **
## CESD.10baseline 0.003019 **
## SmokingStatusFormer Smoker 0.512786
## SmokingStatusNever Smoked 0.071284 .
## SmokingStatusOccasional Smoker 0.619858
## RelationshipstatusMarried 0.023873 *
## RelationshipstatusSeparated 0.582676
## RelationshipstatusSingle 0.671102
## RelationshipstatusWidowed 0.702919
## LivingstatusAssisted Living 0.006726 **
## LivingstatusHouse 0.418608
## LivingstatusOther 0.901994
## AnxietyYes 0.943542
## MoodDisordYes 0.104185
## Chronicconditions 0.035338 *
## RVLT_Immediate_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.204590
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
anova(modelRVLT_imm_adj10)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## timefactor 382.6 382.6 1 10128 51.3079 8.445e-13
## Pandemic 39.6 39.6 1 10316 5.3115 0.0212044
## Age 362.4 362.4 1 10332 48.6029 3.326e-12
## Sex 580.3 580.3 1 10307 77.8248 < 2.2e-16
## Education 168.2 56.1 3 10336 7.5176 5.061e-05
## Ethnicity 107.5 107.5 1 10257 14.4121 0.0001477
## IncomeLevel 332.4 83.1 4 10293 11.1444 5.086e-09
## BMI 64.6 64.6 1 10258 8.6640 0.0032529
## CESD.10baseline 65.6 65.6 1 10324 8.8002 0.0030190
## SmokingStatus 57.5 19.2 3 10300 2.5720 0.0523205
## Relationshipstatus 85.1 21.3 4 10351 2.8516 0.0224059
## Livingstatus 67.0 22.3 3 10277 2.9955 0.0295176
## Anxiety 0.0 0.0 1 10315 0.0050 0.9435424
## MoodDisord 19.7 19.7 1 10307 2.6407 0.1041850
## Chronicconditions 33.0 33.0 1 10314 4.4299 0.0353380
## RVLT_Immediate_Normedbaseline 16437.2 16437.2 1 10327 2204.4451 < 2.2e-16
## timefactor:Pandemic 12.0 12.0 1 10128 1.6095 0.2045900
##
## timefactor ***
## Pandemic *
## Age ***
## Sex ***
## Education ***
## Ethnicity ***
## IncomeLevel ***
## BMI **
## CESD.10baseline **
## SmokingStatus .
## Relationshipstatus *
## Livingstatus *
## Anxiety
## MoodDisord
## Chronicconditions *
## RVLT_Immediate_Normedbaseline ***
## timefactor:Pandemic
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
5.1.2) Estimated marginal means
lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor)
## timefactor = 1:
## Pandemic lsmean SE df lower.CL
## FU2 data collected after COVID-19 10.29666 0.1827283 10768.87 9.938482
## FU2 data collected before COVID-19 10.47788 0.1831489 10701.03 10.118874
## upper.CL
## 10.65484
## 10.83689
##
## timefactor = 2:
## Pandemic lsmean SE df lower.CL
## FU2 data collected after COVID-19 10.62379 0.1829312 10800.00 10.265211
## FU2 data collected before COVID-19 10.70656 0.1830793 10690.38 10.347696
## upper.CL
## 10.98237
## 11.06544
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df t.ratio p.value
## -0.18121676 0.06889484 17828.92 -2.630 0.0085
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df t.ratio p.value
## -0.08277509 0.06945803 17963.18 -1.192 0.2334
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df lower.CL upper.CL
## -0.18121676 0.06889484 17828.92 -0.3162573 -0.04617619
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df lower.CL upper.CL
## -0.08277509 0.06945803 17963.18 -0.2189195 0.05336933
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
5.1.3) Graph of estimated marginal means
RVLTimmediate_lsmeans_adj10 <- summary(lsmeans(modelRVLT_imm_adj10, ~timefactor|Pandemic))
RVLTimmediate_lsmeans_adj10$Time<-NA
RVLTimmediate_lsmeans_adj10$Time[RVLTimmediate_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj10$Time[RVLTimmediate_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
5.1.4) Planned contrasts
Test whether differences between cohorts at FU1 and FU2 are
significant
lsmeans.RVLTImm10 <- lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor)
contrast(lsmeans.RVLTImm10,list(c1st),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.09844167 0.07759502 10134.68
## t.ratio p.value
## 1.269 0.2046
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
5.2) RVLT Delayed Recall
5.2.1) Model
modelRVLT_del_adj10<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + RVLT_Delayed_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelRVLT_del_adj10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + RVLT_Delayed_Normedbaseline + (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 103623.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9202 -0.5513 -0.0366 0.5075 4.1570
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.099 2.025
## Residual 6.955 2.637
## Number of obs: 20030, groups: ID, 10379
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 7.308e+00 3.505e-01
## timefactor2 5.842e-01 5.580e-02
## PandemicFU2 data collected before COVID-19 2.840e-01 6.748e-02
## Age -1.378e-02 3.345e-03
## SexM -4.890e-01 6.101e-02
## EducationHigh School Diploma 3.149e-01 8.587e-02
## EducationLess than High School Diploma 3.300e-01 1.192e-01
## EducationSome College 2.152e-01 1.071e-01
## EthnicityWhite 7.508e-01 1.691e-01
## IncomeLevel>$150k 5.890e-01 1.615e-01
## IncomeLevel$100-150k 4.381e-01 1.286e-01
## IncomeLevel$20-50k 2.284e-01 8.397e-02
## IncomeLevel$50-100k 5.421e-01 9.082e-02
## BMI -1.686e-02 5.563e-03
## CESD.10baseline -2.056e-02 6.818e-03
## SmokingStatusFormer Smoker 6.218e-02 1.149e-01
## SmokingStatusNever Smoked 2.801e-01 1.199e-01
## SmokingStatusOccasional Smoker 1.684e-02 2.345e-01
## RelationshipstatusMarried 5.088e-03 1.012e-01
## RelationshipstatusSeparated -1.370e-01 1.976e-01
## RelationshipstatusSingle -7.281e-02 1.367e-01
## RelationshipstatusWidowed -2.265e-01 1.358e-01
## LivingstatusAssisted Living -1.246e+00 3.977e-01
## LivingstatusHouse 1.297e-01 9.025e-02
## LivingstatusOther -4.025e-02 3.104e-01
## AnxietyYes 8.321e-02 1.188e-01
## MoodDisordYes -1.630e-01 8.670e-02
## Chronicconditions -4.247e-02 1.387e-02
## RVLT_Delayed_Normedbaseline 3.927e-01 7.464e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -3.051e-01 7.539e-02
## df t value
## (Intercept) 1.047e+04 20.854
## timefactor2 1.013e+04 10.468
## PandemicFU2 data collected before COVID-19 1.762e+04 4.209
## Age 1.034e+04 -4.120
## SexM 1.029e+04 -8.015
## EducationHigh School Diploma 1.032e+04 3.667
## EducationLess than High School Diploma 1.050e+04 2.769
## EducationSome College 1.021e+04 2.009
## EthnicityWhite 1.034e+04 4.439
## IncomeLevel>$150k 1.029e+04 3.648
## IncomeLevel$100-150k 1.027e+04 3.406
## IncomeLevel$20-50k 1.033e+04 2.721
## IncomeLevel$50-100k 1.030e+04 5.968
## BMI 1.022e+04 -3.031
## CESD.10baseline 1.034e+04 -3.016
## SmokingStatusFormer Smoker 1.035e+04 0.541
## SmokingStatusNever Smoked 1.034e+04 2.337
## SmokingStatusOccasional Smoker 1.027e+04 0.072
## RelationshipstatusMarried 1.030e+04 0.050
## RelationshipstatusSeparated 1.041e+04 -0.694
## RelationshipstatusSingle 1.027e+04 -0.533
## RelationshipstatusWidowed 1.031e+04 -1.668
## LivingstatusAssisted Living 1.041e+04 -3.133
## LivingstatusHouse 1.032e+04 1.437
## LivingstatusOther 1.033e+04 -0.130
## AnxietyYes 1.029e+04 0.700
## MoodDisordYes 1.028e+04 -1.880
## Chronicconditions 1.030e+04 -3.062
## RVLT_Delayed_Normedbaseline 1.029e+04 52.613
## timefactor2:PandemicFU2 data collected before COVID-19 1.006e+04 -4.047
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 2.58e-05 ***
## Age 3.82e-05 ***
## SexM 1.22e-15 ***
## EducationHigh School Diploma 0.000247 ***
## EducationLess than High School Diploma 0.005628 **
## EducationSome College 0.044599 *
## EthnicityWhite 9.12e-06 ***
## IncomeLevel>$150k 0.000266 ***
## IncomeLevel$100-150k 0.000662 ***
## IncomeLevel$20-50k 0.006528 **
## IncomeLevel$50-100k 2.48e-09 ***
## BMI 0.002443 **
## CESD.10baseline 0.002564 **
## SmokingStatusFormer Smoker 0.588373
## SmokingStatusNever Smoked 0.019480 *
## SmokingStatusOccasional Smoker 0.942767
## RelationshipstatusMarried 0.959889
## RelationshipstatusSeparated 0.487961
## RelationshipstatusSingle 0.594311
## RelationshipstatusWidowed 0.095282 .
## LivingstatusAssisted Living 0.001738 **
## LivingstatusHouse 0.150622
## LivingstatusOther 0.896827
## AnxietyYes 0.483834
## MoodDisordYes 0.060177 .
## Chronicconditions 0.002203 **
## RVLT_Delayed_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 5.23e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
anova(modelRVLT_del_adj10)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## timefactor 911.8 911.8 1 10061 131.1009 < 2.2e-16 ***
## Pandemic 37.9 37.9 1 10308 5.4531 0.0195524 *
## Age 118.1 118.1 1 10342 16.9759 3.815e-05 ***
## Sex 446.7 446.7 1 10295 64.2369 1.223e-15 ***
## Education 140.2 46.7 3 10344 6.7190 0.0001589 ***
## Ethnicity 137.1 137.1 1 10343 19.7076 9.118e-06 ***
## IncomeLevel 295.0 73.8 4 10284 10.6061 1.417e-08 ***
## BMI 63.9 63.9 1 10222 9.1877 0.0024425 **
## CESD.10baseline 63.3 63.3 1 10340 9.0991 0.0025636 **
## SmokingStatus 100.6 33.5 3 10295 4.8210 0.0023486 **
## Relationshipstatus 37.4 9.3 4 10334 1.3435 0.2511201
## Livingstatus 97.0 32.3 3 10358 4.6502 0.0029853 **
## Anxiety 3.4 3.4 1 10289 0.4902 0.4838338
## MoodDisord 24.6 24.6 1 10279 3.5333 0.0601766 .
## Chronicconditions 65.2 65.2 1 10299 9.3769 0.0022030 **
## RVLT_Delayed_Normedbaseline 19251.5 19251.5 1 10292 2768.1443 < 2.2e-16 ***
## timefactor:Pandemic 113.9 113.9 1 10061 16.3755 5.234e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
5.2.2) Estimated marginal means
lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor)
## timefactor = 1:
## Pandemic lsmean SE df lower.CL
## FU2 data collected after COVID-19 10.37762 0.1800165 10808.72 10.02476
## FU2 data collected before COVID-19 10.66160 0.1804340 10738.92 10.30792
## upper.CL
## 10.73049
## 11.01528
##
## timefactor = 2:
## Pandemic lsmean SE df lower.CL
## FU2 data collected after COVID-19 10.96180 0.1802059 10838.82 10.60856
## FU2 data collected before COVID-19 10.94068 0.1803483 10725.46 10.58717
## upper.CL
## 11.31503
## 11.29420
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df t.ratio p.value
## -0.28397783 0.06747662 17616.67 -4.209 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df t.ratio p.value
## 0.02111468 0.06800440 17749.18 0.310 0.7562
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df lower.CL upper.CL
## -0.28397783 0.06747662 17616.67 -0.4162387 -0.151717
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df lower.CL upper.CL
## 0.02111468 0.06800440 17749.18 -0.1121806 0.154410
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
5.2.3) Graph of estimated marginal means
RVLTdelayed_lsmeans_adj10 <- summary(lsmeans(modelRVLT_del_adj10, ~timefactor|Pandemic))
RVLTdelayed_lsmeans_adj10$Time<-NA
RVLTdelayed_lsmeans_adj10$Time[RVLTdelayed_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj10$Time[RVLTdelayed_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
5.2.4) Planned contrasts
Test whether differences between cohorts at FU1 and FU2 are
significant
lsmeans.RVLTDel10 <- lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor)
contrast(lsmeans.RVLTDel10,list(c1st),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.3050925 0.07539398 10051.4
## t.ratio p.value
## 4.047 0.0001
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
5.3) Mental Alteration Test
5.3.1) Model
modelMAT_adj10<- lmer(MAT_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + MAT_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelMAT_adj10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + MAT_Normedbaseline + (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 98080.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4255 -0.5244 -0.0823 0.3678 4.8694
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.322 1.524
## Residual 8.535 2.921
## Number of obs: 18841, groups: ID, 10262
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 7.772e+00 3.383e-01
## timefactor2 -1.408e+00 6.399e-02
## PandemicFU2 data collected before COVID-19 -5.110e-01 6.897e-02
## Age -1.182e-02 3.216e-03
## SexM -1.185e+00 5.842e-02
## EducationHigh School Diploma -4.450e-03 8.238e-02
## EducationLess than High School Diploma -1.563e-01 1.158e-01
## EducationSome College -1.551e-01 1.021e-01
## EthnicityWhite 9.209e-01 1.630e-01
## IncomeLevel>$150k 1.290e-01 1.546e-01
## IncomeLevel$100-150k 1.601e-01 1.233e-01
## IncomeLevel$20-50k 1.050e-01 8.082e-02
## IncomeLevel$50-100k 1.458e-01 8.740e-02
## BMI -1.546e-02 5.326e-03
## CESD.10baseline -1.402e-02 6.537e-03
## SmokingStatusFormer Smoker 5.973e-02 1.099e-01
## SmokingStatusNever Smoked -6.673e-02 1.147e-01
## SmokingStatusOccasional Smoker 1.143e-01 2.245e-01
## RelationshipstatusMarried 3.628e-02 9.715e-02
## RelationshipstatusSeparated -7.207e-02 1.887e-01
## RelationshipstatusSingle 5.128e-01 1.312e-01
## RelationshipstatusWidowed -4.490e-02 1.311e-01
## LivingstatusAssisted Living -2.329e-01 3.840e-01
## LivingstatusHouse -2.095e-01 8.678e-02
## LivingstatusOther -7.092e-01 2.976e-01
## AnxietyYes 1.399e-01 1.138e-01
## MoodDisordYes 1.001e-02 8.293e-02
## Chronicconditions -4.277e-02 1.336e-02
## MAT_Normedbaseline 4.411e-01 7.721e-03
## timefactor2:PandemicFU2 data collected before COVID-19 5.356e-01 8.637e-02
## df t value
## (Intercept) 1.017e+04 22.974
## timefactor2 9.732e+03 -21.999
## PandemicFU2 data collected before COVID-19 1.813e+04 -7.409
## Age 1.014e+04 -3.674
## SexM 9.967e+03 -20.280
## EducationHigh School Diploma 9.974e+03 -0.054
## EducationLess than High School Diploma 1.025e+04 -1.349
## EducationSome College 9.837e+03 -1.519
## EthnicityWhite 1.014e+04 5.650
## IncomeLevel>$150k 9.915e+03 0.835
## IncomeLevel$100-150k 9.935e+03 1.299
## IncomeLevel$20-50k 1.004e+04 1.299
## IncomeLevel$50-100k 9.974e+03 1.668
## BMI 9.816e+03 -2.902
## CESD.10baseline 9.939e+03 -2.144
## SmokingStatusFormer Smoker 9.971e+03 0.544
## SmokingStatusNever Smoked 9.964e+03 -0.582
## SmokingStatusOccasional Smoker 9.855e+03 0.509
## RelationshipstatusMarried 1.005e+04 0.373
## RelationshipstatusSeparated 1.001e+04 -0.382
## RelationshipstatusSingle 1.000e+04 3.910
## RelationshipstatusWidowed 1.013e+04 -0.343
## LivingstatusAssisted Living 1.016e+04 -0.607
## LivingstatusHouse 1.005e+04 -2.415
## LivingstatusOther 9.856e+03 -2.383
## AnxietyYes 9.972e+03 1.229
## MoodDisordYes 9.905e+03 0.121
## Chronicconditions 1.008e+04 -3.201
## MAT_Normedbaseline 1.004e+04 57.130
## timefactor2:PandemicFU2 data collected before COVID-19 9.653e+03 6.201
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 1.32e-13 ***
## Age 0.00024 ***
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.95692
## EducationLess than High School Diploma 0.17734
## EducationSome College 0.12886
## EthnicityWhite 1.65e-08 ***
## IncomeLevel>$150k 0.40393
## IncomeLevel$100-150k 0.19402
## IncomeLevel$20-50k 0.19383
## IncomeLevel$50-100k 0.09538 .
## BMI 0.00371 **
## CESD.10baseline 0.03204 *
## SmokingStatusFormer Smoker 0.58678
## SmokingStatusNever Smoked 0.56073
## SmokingStatusOccasional Smoker 0.61064
## RelationshipstatusMarried 0.70883
## RelationshipstatusSeparated 0.70247
## RelationshipstatusSingle 9.30e-05 ***
## RelationshipstatusWidowed 0.73194
## LivingstatusAssisted Living 0.54416
## LivingstatusHouse 0.01576 *
## LivingstatusOther 0.01720 *
## AnxietyYes 0.21912
## MoodDisordYes 0.90398
## Chronicconditions 0.00138 **
## MAT_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 5.86e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
anova(modelMAT_adj10)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## timefactor 5944.5 5944.5 1 9651.9 696.5100 < 2.2e-16 ***
## Pandemic 173.1 173.1 1 10008.1 20.2828 6.756e-06 ***
## Age 115.2 115.2 1 10143.2 13.4993 0.0002399 ***
## Sex 3510.3 3510.3 1 9967.0 411.2965 < 2.2e-16 ***
## Education 32.6 10.9 3 10020.4 1.2743 0.2813077
## Ethnicity 272.4 272.4 1 10144.8 31.9206 1.649e-08 ***
## IncomeLevel 25.6 6.4 4 9942.1 0.7487 0.5587154
## BMI 71.9 71.9 1 9815.8 8.4241 0.0037111 **
## CESD.10baseline 39.2 39.2 1 9939.1 4.5976 0.0320405 *
## SmokingStatus 42.1 14.0 3 9915.8 1.6430 0.1771420
## Relationshipstatus 209.5 52.4 4 10036.3 6.1367 6.285e-05 ***
## Livingstatus 79.6 26.5 3 10020.5 3.1078 0.0253301 *
## Anxiety 12.9 12.9 1 9972.2 1.5103 0.2191165
## MoodDisord 0.1 0.1 1 9905.2 0.0146 0.9039759
## Chronicconditions 87.4 87.4 1 10079.5 10.2442 0.0013754 **
## MAT_Normedbaseline 27855.4 27855.4 1 10042.1 3263.8035 < 2.2e-16 ***
## timefactor:Pandemic 328.1 328.1 1 9652.6 38.4475 5.855e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
5.3.2) Estimated marginal means
lsmeans(modelMAT_adj10, ~Pandemic|timefactor)
## timefactor = 1:
## Pandemic lsmean SE df lower.CL
## FU2 data collected after COVID-19 10.665980 0.1741807 10709.43 10.324554
## FU2 data collected before COVID-19 10.154966 0.1742890 10591.05 9.813327
## upper.CL
## 11.007407
## 10.496605
##
## timefactor = 2:
## Pandemic lsmean SE df lower.CL
## FU2 data collected after COVID-19 9.258314 0.1740161 10687.80 8.917210
## FU2 data collected before COVID-19 9.282878 0.1741834 10577.03 8.941446
## upper.CL
## 9.599418
## 9.624310
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_adj10, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df t.ratio p.value
## 0.5110145 0.06896917 18128.64 7.409 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df t.ratio p.value
## -0.0245641 0.06933355 18159.72 -0.354 0.7231
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelMAT_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df lower.CL upper.CL
## 0.5110145 0.06896917 18128.64 0.3758284 0.6462006
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df lower.CL upper.CL
## -0.0245641 0.06933355 18159.72 -0.1604644 0.1113362
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
5.3.3) Graph of estimated marginal means
MAT_lsmeans_adj10 <- summary(lsmeans(modelMAT_adj10, ~Pandemic|timefactor))
MAT_lsmeans_adj10$Time<-NA
MAT_lsmeans_adj10$Time[MAT_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj10$Time[MAT_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
5.3.4) Planned contrasts
Test whether differences between cohorts at FU1 and FU2 are
significant
lsmeans.MAT10 <- lsmeans(modelMAT_adj10, ~Pandemic|timefactor)
contrast(lsmeans.MAT10,list(c1st),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.5355786 0.0863762 9673.68
## t.ratio p.value
## -6.201 <.0001
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
5.4) Animal Fluency
5.4.1) Model
modelAnimals_adj10<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + Animal_Fluency_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelAnimals_adj10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + Animal_Fluency_Normedbaseline + (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 94871.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8664 -0.5429 -0.0218 0.5261 4.5657
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.996 1.731
## Residual 3.870 1.967
## Number of obs: 20333, groups: ID, 10403
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 5.746e+00 2.835e-01
## timefactor2 5.077e-02 4.127e-02
## PandemicFU2 data collected before COVID-19 1.944e-01 5.280e-02
## Age -2.582e-02 2.665e-03
## SexM -9.745e-02 4.884e-02
## EducationHigh School Diploma 2.223e-01 6.891e-02
## EducationLess than High School Diploma 7.748e-02 9.497e-02
## EducationSome College 2.309e-01 8.596e-02
## EthnicityWhite 6.059e-01 1.353e-01
## IncomeLevel>$150k 1.162e-01 1.295e-01
## IncomeLevel$100-150k 1.947e-01 1.032e-01
## IncomeLevel$20-50k -1.037e-02 6.726e-02
## IncomeLevel$50-100k 1.725e-01 7.282e-02
## BMI -5.115e-03 4.466e-03
## CESD.10baseline -1.453e-02 5.471e-03
## SmokingStatusFormer Smoker 1.002e-01 9.210e-02
## SmokingStatusNever Smoked 8.221e-02 9.613e-02
## SmokingStatusOccasional Smoker -6.541e-02 1.887e-01
## RelationshipstatusMarried 7.248e-02 8.114e-02
## RelationshipstatusSeparated 2.660e-01 1.583e-01
## RelationshipstatusSingle 5.200e-02 1.097e-01
## RelationshipstatusWidowed 2.219e-02 1.088e-01
## LivingstatusAssisted Living 4.125e-02 3.177e-01
## LivingstatusHouse 2.059e-01 7.241e-02
## LivingstatusOther -4.868e-02 2.478e-01
## AnxietyYes 3.443e-02 9.542e-02
## MoodDisordYes 9.391e-02 6.966e-02
## Chronicconditions -2.005e-02 1.111e-02
## Animal_Fluency_Normedbaseline 5.566e-01 6.452e-03
## timefactor2:PandemicFU2 data collected before COVID-19 9.056e-03 5.573e-02
## df t value
## (Intercept) 1.043e+04 20.267
## timefactor2 1.022e+04 1.230
## PandemicFU2 data collected before COVID-19 1.699e+04 3.682
## Age 1.033e+04 -9.688
## SexM 1.032e+04 -1.995
## EducationHigh School Diploma 1.035e+04 3.226
## EducationLess than High School Diploma 1.043e+04 0.816
## EducationSome College 1.027e+04 2.686
## EthnicityWhite 1.029e+04 4.476
## IncomeLevel>$150k 1.032e+04 0.897
## IncomeLevel$100-150k 1.030e+04 1.887
## IncomeLevel$20-50k 1.033e+04 -0.154
## IncomeLevel$50-100k 1.032e+04 2.368
## BMI 1.028e+04 -1.145
## CESD.10baseline 1.036e+04 -2.656
## SmokingStatusFormer Smoker 1.038e+04 1.088
## SmokingStatusNever Smoked 1.037e+04 0.855
## SmokingStatusOccasional Smoker 1.029e+04 -0.347
## RelationshipstatusMarried 1.036e+04 0.893
## RelationshipstatusSeparated 1.038e+04 1.681
## RelationshipstatusSingle 1.035e+04 0.474
## RelationshipstatusWidowed 1.033e+04 0.204
## LivingstatusAssisted Living 1.029e+04 0.130
## LivingstatusHouse 1.034e+04 2.844
## LivingstatusOther 1.019e+04 -0.196
## AnxietyYes 1.034e+04 0.361
## MoodDisordYes 1.034e+04 1.348
## Chronicconditions 1.030e+04 -1.805
## Animal_Fluency_Normedbaseline 1.031e+04 86.267
## timefactor2:PandemicFU2 data collected before COVID-19 1.015e+04 0.162
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 0.218714
## PandemicFU2 data collected before COVID-19 0.000232 ***
## Age < 2e-16 ***
## SexM 0.046025 *
## EducationHigh School Diploma 0.001259 **
## EducationLess than High School Diploma 0.414623
## EducationSome College 0.007245 **
## EthnicityWhite 7.67e-06 ***
## IncomeLevel>$150k 0.369861
## IncomeLevel$100-150k 0.059231 .
## IncomeLevel$20-50k 0.877467
## IncomeLevel$50-100k 0.017881 *
## BMI 0.252095
## CESD.10baseline 0.007920 **
## SmokingStatusFormer Smoker 0.276730
## SmokingStatusNever Smoked 0.392461
## SmokingStatusOccasional Smoker 0.728827
## RelationshipstatusMarried 0.371739
## RelationshipstatusSeparated 0.092828 .
## RelationshipstatusSingle 0.635404
## RelationshipstatusWidowed 0.838382
## LivingstatusAssisted Living 0.896701
## LivingstatusHouse 0.004464 **
## LivingstatusOther 0.844262
## AnxietyYes 0.718279
## MoodDisordYes 0.177633
## Chronicconditions 0.071161 .
## Animal_Fluency_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.870926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
anova(modelAnimals_adj10)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## timefactor 15.2 15.2 1 10153 3.9375 0.047247
## Pandemic 75.3 75.3 1 10337 19.4654 1.035e-05
## Age 363.2 363.2 1 10332 93.8618 < 2.2e-16
## Sex 15.4 15.4 1 10316 3.9817 0.046025
## Education 60.2 20.1 3 10349 5.1889 0.001398
## Ethnicity 77.5 77.5 1 10293 20.0379 7.674e-06
## IncomeLevel 53.8 13.5 4 10317 3.4779 0.007609
## BMI 5.1 5.1 1 10277 1.3118 0.252095
## CESD.10baseline 27.3 27.3 1 10355 7.0542 0.007920
## SmokingStatus 7.8 2.6 3 10321 0.6677 0.571781
## Relationshipstatus 12.1 3.0 4 10348 0.7840 0.535339
## Livingstatus 34.9 11.6 3 10269 3.0091 0.028977
## Anxiety 0.5 0.5 1 10338 0.1302 0.718279
## MoodDisord 7.0 7.0 1 10340 1.8176 0.177633
## Chronicconditions 12.6 12.6 1 10303 3.2567 0.071161
## Animal_Fluency_Normedbaseline 28800.0 28800.0 1 10311 7442.0627 < 2.2e-16
## timefactor:Pandemic 0.1 0.1 1 10153 0.0264 0.870926
##
## timefactor *
## Pandemic ***
## Age ***
## Sex *
## Education **
## Ethnicity ***
## IncomeLevel **
## BMI
## CESD.10baseline **
## SmokingStatus
## Relationshipstatus
## Livingstatus *
## Anxiety
## MoodDisord
## Chronicconditions .
## Animal_Fluency_Normedbaseline ***
## timefactor:Pandemic
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
5.4.2) Estimated marginal means
lsmeans(modelAnimals_adj10, ~Pandemic|timefactor)
## timefactor = 1:
## Pandemic lsmean SE df lower.CL
## FU2 data collected after COVID-19 10.84939 0.1438453 10712.42 10.56743
## FU2 data collected before COVID-19 11.04381 0.1441413 10644.83 10.76127
## upper.CL
## 11.13136
## 11.32636
##
## timefactor = 2:
## Pandemic lsmean SE df lower.CL
## FU2 data collected after COVID-19 10.90016 0.1439582 10735.89 10.61797
## FU2 data collected before COVID-19 11.10364 0.1441642 10649.60 10.82105
## upper.CL
## 11.18234
## 11.38623
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_adj10, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df t.ratio p.value
## -0.1944217 0.05280075 17004.38 -3.682 0.0002
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df t.ratio p.value
## -0.2034773 0.05321545 17176.59 -3.824 0.0001
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelAnimals_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df lower.CL upper.CL
## -0.1944217 0.05280075 17004.38 -0.2979167 -0.09092677
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df lower.CL upper.CL
## -0.2034773 0.05321545 17176.59 -0.3077850 -0.09916961
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
5.4.3) Graph of estimated marginal means
Animals_lsmeans_adj10 <- summary(lsmeans(modelAnimals_adj10, ~timefactor|Pandemic))
Animals_lsmeans_adj10$Time<-NA
Animals_lsmeans_adj10$Time[Animals_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj10$Time[Animals_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
5.4.4) Planned contrasts
Test whether differences between cohorts at FU1 and FU2 are
significant
lsmeans.Animals10 <- lsmeans(modelAnimals_adj10, ~Pandemic|timefactor)
contrast(lsmeans.Animals10,list(c1st),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.009055605 0.05573164 10171.64
## t.ratio p.value
## -0.162 0.8709
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: kenward-roger
